/**
* Classifies the given test instance. The instance has to belong to a dataset when it's being
* classified. Note that a classifier MUST implement either this or distributionForInstance().
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or Utils.missingValue() if no
* prediction is made
* @exception Exception if an error occurred during the prediction
*/
@Override
public double classifyInstance(Instance instance) throws Exception {
double[] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double max = 0;
int maxIndex = 0;
for (int i = 0; i < dist.length; i++) {
if (dist[i] > max) {
maxIndex = i;
max = dist[i];
}
}
if (max > 0) {
return maxIndex;
} else {
return Utils.missingValue();
}
case Attribute.NUMERIC:
case Attribute.DATE:
return dist[0];
default:
return Utils.missingValue();
}
}
/**
* Convert a single instance over. The converted instance is added to the end of the output queue.
*
* @param instance the instance to convert
*/
protected void convertInstance(Instance instance) {
int index = 0;
double[] vals = new double[outputFormatPeek().numAttributes()];
// Copy and convert the values
for (int i = 0; i < getInputFormat().numAttributes(); i++) {
if (m_DiscretizeCols.isInRange(i) && getInputFormat().attribute(i).isNumeric()) {
int j;
double currentVal = instance.value(i);
if (m_CutPoints[i] == null) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
vals[index] = 0;
}
index++;
} else {
if (!m_MakeBinary) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (currentVal <= m_CutPoints[i][j]) {
break;
}
}
vals[index] = j;
}
index++;
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else if (currentVal <= m_CutPoints[i][j]) {
vals[index] = 0;
} else {
vals[index] = 1;
}
index++;
}
}
}
} else {
vals[index] = instance.value(i);
index++;
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
/**
* Adds the prediction intervals as additional attributes at the end. Since classifiers can
* returns varying number of intervals per instance, the dataset is filled with missing values for
* non-existing intervals.
*/
protected void addPredictionIntervals() {
int maxNum;
int num;
int i;
int n;
FastVector preds;
FastVector atts;
Instances data;
Instance inst;
Instance newInst;
double[] values;
double[][] predInt;
// determine the maximum number of intervals
maxNum = 0;
preds = m_Evaluation.predictions();
for (i = 0; i < preds.size(); i++) {
num = ((NumericPrediction) preds.elementAt(i)).predictionIntervals().length;
if (num > maxNum) maxNum = num;
}
// create new header
atts = new FastVector();
for (i = 0; i < m_PlotInstances.numAttributes(); i++)
atts.addElement(m_PlotInstances.attribute(i));
for (i = 0; i < maxNum; i++) {
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-lowerBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-upperBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-width"));
}
data = new Instances(m_PlotInstances.relationName(), atts, m_PlotInstances.numInstances());
data.setClassIndex(m_PlotInstances.classIndex());
// update data
for (i = 0; i < m_PlotInstances.numInstances(); i++) {
inst = m_PlotInstances.instance(i);
// copy old values
values = new double[data.numAttributes()];
System.arraycopy(inst.toDoubleArray(), 0, values, 0, inst.numAttributes());
// add interval data
predInt = ((NumericPrediction) preds.elementAt(i)).predictionIntervals();
for (n = 0; n < maxNum; n++) {
if (n < predInt.length) {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = predInt[n][0];
values[m_PlotInstances.numAttributes() + n * 3 + 1] = predInt[n][1];
values[m_PlotInstances.numAttributes() + n * 3 + 2] = predInt[n][1] - predInt[n][0];
} else {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 1] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 2] = Utils.missingValue();
}
}
// create new Instance
newInst = new DenseInstance(inst.weight(), values);
data.add(newInst);
}
m_PlotInstances = data;
}
/**
* Constructs an instance suitable for passing to the model for scoring
*
* @param incoming the incoming instance
* @return an instance with values mapped to be consistent with what the model is expecting
*/
protected Instance mapIncomingFieldsToModelFields(Instance incoming) {
Instances modelHeader = m_model.getHeader();
double[] vals = new double[modelHeader.numAttributes()];
for (int i = 0; i < modelHeader.numAttributes(); i++) {
if (m_attributeMap[i] < 0) {
// missing or type mismatch
vals[i] = Utils.missingValue();
continue;
}
Attribute modelAtt = modelHeader.attribute(i);
Attribute incomingAtt = incoming.dataset().attribute(m_attributeMap[i]);
if (incoming.isMissing(incomingAtt.index())) {
vals[i] = Utils.missingValue();
continue;
}
if (modelAtt.isNumeric()) {
vals[i] = incoming.value(m_attributeMap[i]);
} else if (modelAtt.isNominal()) {
String incomingVal = incoming.stringValue(m_attributeMap[i]);
int modelIndex = modelAtt.indexOfValue(incomingVal);
if (modelIndex < 0) {
vals[i] = Utils.missingValue();
} else {
vals[i] = modelIndex;
}
} else if (modelAtt.isString()) {
vals[i] = 0;
modelAtt.setStringValue(incoming.stringValue(m_attributeMap[i]));
}
}
if (modelHeader.classIndex() >= 0) {
// set class to missing value
vals[modelHeader.classIndex()] = Utils.missingValue();
}
Instance newInst = null;
if (incoming instanceof SparseInstance) {
newInst = new SparseInstance(incoming.weight(), vals);
} else {
newInst = new DenseInstance(incoming.weight(), vals);
}
newInst.setDataset(modelHeader);
return newInst;
}
/**
* Convert an input instance
*
* @param current the input instance to convert
* @return a transformed instance
* @throws Exception if a problem occurs
*/
protected Instance convertInstance(Instance current) throws Exception {
double[] vals = new double[getOutputFormat().numAttributes()];
int index = 0;
for (int j = 0; j < current.numAttributes(); j++) {
if (j != current.classIndex()) {
if (m_unchanged != null && m_unchanged.attribute(current.attribute(j).name()) != null) {
vals[index++] = current.value(j);
} else {
Estimator[] estForAtt = m_estimatorLookup.get(current.attribute(j).name());
for (int k = 0; k < current.classAttribute().numValues(); k++) {
if (current.isMissing(j)) {
vals[index++] = Utils.missingValue();
} else {
double e = estForAtt[k].getProbability(current.value(j));
vals[index++] = e;
}
}
}
}
}
vals[vals.length - 1] = current.classValue();
DenseInstance instNew = new DenseInstance(current.weight(), vals);
return instNew;
}
/**
* generates an instance out of the given data
*
* @param tc the statistics
* @param prob the probability
* @return the generated instance
*/
private Instance makeInstance(TwoClassStats tc, double prob) {
int count = 0;
double[] vals = new double[13];
vals[count++] = tc.getTruePositive();
vals[count++] = tc.getFalseNegative();
vals[count++] = tc.getFalsePositive();
vals[count++] = tc.getTrueNegative();
vals[count++] = tc.getFalsePositiveRate();
vals[count++] = tc.getTruePositiveRate();
vals[count++] = tc.getPrecision();
vals[count++] = tc.getRecall();
vals[count++] = tc.getFallout();
vals[count++] = tc.getFMeasure();
double ss =
(tc.getTruePositive() + tc.getFalsePositive())
/ (tc.getTruePositive()
+ tc.getFalsePositive()
+ tc.getTrueNegative()
+ tc.getFalseNegative());
vals[count++] = ss;
double expectedByChance = (ss * (tc.getTruePositive() + tc.getFalseNegative()));
if (expectedByChance < 1) {
vals[count++] = Utils.missingValue();
} else {
vals[count++] = tc.getTruePositive() / expectedByChance;
}
vals[count++] = prob;
return new DenseInstance(1.0, vals);
}
/**
* Calculates the area under the precision-recall curve (AUPRC).
*
* @param tcurve a previously extracted threshold curve Instances.
* @return the PRC area, or Double.NaN if you don't pass in a ThresholdCurve generated Instances.
*/
public static double getPRCArea(Instances tcurve) {
final int n = tcurve.numInstances();
if (!RELATION_NAME.equals(tcurve.relationName()) || (n == 0)) {
return Double.NaN;
}
final int pInd = tcurve.attribute(PRECISION_NAME).index();
final int rInd = tcurve.attribute(RECALL_NAME).index();
final double[] pVals = tcurve.attributeToDoubleArray(pInd);
final double[] rVals = tcurve.attributeToDoubleArray(rInd);
double area = 0;
double xlast = rVals[n - 1];
// start from the first real p/r pair (not the artificial zero point)
for (int i = n - 2; i >= 0; i--) {
double recallDelta = rVals[i] - xlast;
area += (pVals[i] * recallDelta);
xlast = rVals[i];
}
if (area == 0) {
return Utils.missingValue();
}
return area;
}
/**
* Input an instance for filtering. Ordinarily the instance is processed and made available for
* output immediately. Some filters require all instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be collected with output().
* @throws IllegalStateException if no input structure has been defined.
*/
@Override
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (getOutputFormat().numAttributes() == 0) {
return false;
}
if (m_selectedAttributes.length == 0) {
push(instance);
} else {
double vals[] = new double[getOutputFormat().numAttributes()];
for (int i = 0; i < instance.numAttributes(); i++) {
double currentV = instance.value(i);
if (!m_selectedCols.isInRange(i)) {
vals[i] = currentV;
} else {
if (currentV == Utils.missingValue()) {
vals[i] = currentV;
} else {
String currentS = instance.attribute(i).value((int) currentV);
String replace =
m_ignoreCase ? m_renameMap.get(currentS.toLowerCase()) : m_renameMap.get(currentS);
if (replace == null) {
vals[i] = currentV;
} else {
vals[i] = getOutputFormat().attribute(i).indexOfValue(replace);
}
}
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
return true;
}
/**
* Method for building an Id3 tree.
*
* @param data the training data
* @exception Exception if decision tree can't be built successfully
*/
private void makeTree(Instances data) throws Exception {
// Check if no instances have reached this node.
if (data.numInstances() == 0) {
m_Attribute = null;
m_ClassValue = Utils.missingValue();
m_Distribution = new double[data.numClasses()];
return;
}
// Compute attribute with maximum information gain.
double[] infoGains = new double[data.numAttributes()];
Enumeration attEnum = data.enumerateAttributes();
while (attEnum.hasMoreElements()) {
Attribute att = (Attribute) attEnum.nextElement();
infoGains[att.index()] = computeInfoGain(data, att);
}
m_Attribute = data.attribute(Utils.maxIndex(infoGains));
// Make leaf if information gain is zero.
// Otherwise create successors.
if (Utils.eq(infoGains[m_Attribute.index()], 0)) {
m_Attribute = null;
m_Distribution = new double[data.numClasses()];
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
m_Distribution[(int) inst.classValue()]++;
}
Utils.normalize(m_Distribution);
m_ClassValue = Utils.maxIndex(m_Distribution);
m_ClassAttribute = data.classAttribute();
} else {
Instances[] splitData = splitData(data, m_Attribute);
m_Successors = new Id3[m_Attribute.numValues()];
for (int j = 0; j < m_Attribute.numValues(); j++) {
m_Successors[j] = new Id3();
m_Successors[j].makeTree(splitData[j]);
}
}
}
/**
* Return the full data set. If the structure hasn't yet been determined by a call to getStructure
* then method should do so before processing the rest of the data set.
*
* @return the structure of the data set as an empty set of Instances
* @exception IOException if there is no source or parsing fails
*/
@Override
public Instances getDataSet() throws IOException {
if ((m_sourceFile == null) && (m_sourceReader == null)) {
throw new IOException("No source has been specified");
}
if (m_structure == null) {
getStructure();
}
if (m_st == null) {
m_st = new StreamTokenizer(m_sourceReader);
initTokenizer(m_st);
}
m_st.ordinaryChar(m_FieldSeparator.charAt(0));
m_cumulativeStructure = new ArrayList<Hashtable<Object, Integer>>(m_structure.numAttributes());
for (int i = 0; i < m_structure.numAttributes(); i++) {
m_cumulativeStructure.add(new Hashtable<Object, Integer>());
}
m_cumulativeInstances = new ArrayList<ArrayList<Object>>();
ArrayList<Object> current;
while ((current = getInstance(m_st)) != null) {
m_cumulativeInstances.add(current);
}
ArrayList<Attribute> atts = new ArrayList<Attribute>(m_structure.numAttributes());
for (int i = 0; i < m_structure.numAttributes(); i++) {
String attname = m_structure.attribute(i).name();
Hashtable<Object, Integer> tempHash = m_cumulativeStructure.get(i);
if (tempHash.size() == 0) {
atts.add(new Attribute(attname));
} else {
if (m_StringAttributes.isInRange(i)) {
atts.add(new Attribute(attname, (ArrayList<String>) null));
} else {
ArrayList<String> values = new ArrayList<String>(tempHash.size());
// add dummy objects in order to make the ArrayList's size == capacity
for (int z = 0; z < tempHash.size(); z++) {
values.add("dummy");
}
Enumeration e = tempHash.keys();
while (e.hasMoreElements()) {
Object ob = e.nextElement();
// if (ob instanceof Double) {
int index = ((Integer) tempHash.get(ob)).intValue();
String s = ob.toString();
if (s.startsWith("'") || s.startsWith("\"")) s = s.substring(1, s.length() - 1);
values.set(index, new String(s));
// }
}
atts.add(new Attribute(attname, values));
}
}
}
// make the instances
String relationName;
if (m_sourceFile != null)
relationName = (m_sourceFile.getName()).replaceAll("\\.[cC][sS][vV]$", "");
else relationName = "stream";
Instances dataSet = new Instances(relationName, atts, m_cumulativeInstances.size());
for (int i = 0; i < m_cumulativeInstances.size(); i++) {
current = m_cumulativeInstances.get(i);
double[] vals = new double[dataSet.numAttributes()];
for (int j = 0; j < current.size(); j++) {
Object cval = current.get(j);
if (cval instanceof String) {
if (((String) cval).compareTo(m_MissingValue) == 0) {
vals[j] = Utils.missingValue();
} else {
if (dataSet.attribute(j).isString()) {
vals[j] = dataSet.attribute(j).addStringValue((String) cval);
} else if (dataSet.attribute(j).isNominal()) {
// find correct index
Hashtable<Object, Integer> lookup = m_cumulativeStructure.get(j);
int index = ((Integer) lookup.get(cval)).intValue();
vals[j] = index;
} else {
throw new IllegalStateException(
"Wrong attribute type at position " + (i + 1) + "!!!");
}
}
} else if (dataSet.attribute(j).isNominal()) {
// find correct index
Hashtable<Object, Integer> lookup = m_cumulativeStructure.get(j);
int index = ((Integer) lookup.get(cval)).intValue();
vals[j] = index;
} else if (dataSet.attribute(j).isString()) {
vals[j] = dataSet.attribute(j).addStringValue("" + cval);
} else {
vals[j] = ((Double) cval).doubleValue();
}
}
dataSet.add(new DenseInstance(1.0, vals));
}
m_structure = new Instances(dataSet, 0);
setRetrieval(BATCH);
m_cumulativeStructure = null; // conserve memory
// close the stream
m_sourceReader.close();
return dataSet;
}
protected Instance makeInstance() throws IOException {
if (m_current == null) {
return null;
}
double[] vals = new double[m_structure.numAttributes()];
for (int i = 0; i < m_structure.numAttributes(); i++) {
Object val = m_current.get(i);
if (val.toString().equals("?")) {
vals[i] = Utils.missingValue();
} else if (m_structure.attribute(i).isString()) {
vals[i] = 0;
m_structure.attribute(i).setStringValue(Utils.unquote(val.toString()));
} else if (m_structure.attribute(i).isDate()) {
String format = m_structure.attribute(i).getDateFormat();
SimpleDateFormat sdf = new SimpleDateFormat(format);
String dateVal = Utils.unquote(val.toString());
try {
vals[i] = sdf.parse(dateVal).getTime();
} catch (ParseException e) {
throw new IOException(
"Unable to parse date value "
+ dateVal
+ " using date format "
+ format
+ " for date attribute "
+ m_structure.attribute(i)
+ " (line: "
+ m_rowCount
+ ")");
}
} else if (m_structure.attribute(i).isNumeric()) {
try {
Double v = Double.parseDouble(val.toString());
vals[i] = v.doubleValue();
} catch (NumberFormatException ex) {
throw new IOException(
"Was expecting a number for attribute "
+ m_structure.attribute(i).name()
+ " but read "
+ val.toString()
+ " instead. (line: "
+ m_rowCount
+ ")");
}
} else {
// nominal
double index = m_structure.attribute(i).indexOfValue(Utils.unquote(val.toString()));
if (index < 0) {
throw new IOException(
"Read unknown nominal value "
+ val.toString()
+ "for attribute "
+ m_structure.attribute(i).name()
+ " (line: "
+ m_rowCount
+ "). Try increasing the size of the memory buffer"
+ " (-B option) or explicitly specify legal nominal values with "
+ "the -L option.");
}
vals[i] = index;
}
}
DenseInstance inst = new DenseInstance(1.0, vals);
inst.setDataset(m_structure);
return inst;
}
/**
* Convert a single instance over. The converted instance is added to the end of the output queue.
*
* @param instance the instance to convert
* @throws Exception if instance cannot be converted
*/
private void convertInstance(Instance instance) throws Exception {
Instance inst = null;
HashMap symbols = new HashMap(5);
if (instance instanceof SparseInstance) {
double[] newVals = new double[instance.numAttributes()];
int[] newIndices = new int[instance.numAttributes()];
double[] vals = instance.toDoubleArray();
int ind = 0;
double value;
for (int j = 0; j < instance.numAttributes(); j++) {
if (m_SelectCols.isInRange(j)) {
if (instance.attribute(j).isNumeric()
&& (!Utils.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
symbols.put("A", new Double(vals[j]));
symbols.put("MAX", new Double(m_attStats[j].numericStats.max));
symbols.put("MIN", new Double(m_attStats[j].numericStats.min));
symbols.put("MEAN", new Double(m_attStats[j].numericStats.mean));
symbols.put("SD", new Double(m_attStats[j].numericStats.stdDev));
symbols.put("COUNT", new Double(m_attStats[j].numericStats.count));
symbols.put("SUM", new Double(m_attStats[j].numericStats.sum));
symbols.put("SUMSQUARED", new Double(m_attStats[j].numericStats.sumSq));
value = eval(symbols);
if (Double.isNaN(value) || Double.isInfinite(value)) {
System.err.println("WARNING:Error in evaluating the expression: missing value set");
value = Utils.missingValue();
}
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
}
} else {
value = vals[j];
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
}
}
double[] tempVals = new double[ind];
int[] tempInd = new int[ind];
System.arraycopy(newVals, 0, tempVals, 0, ind);
System.arraycopy(newIndices, 0, tempInd, 0, ind);
inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
} else {
double[] vals = instance.toDoubleArray();
for (int j = 0; j < getInputFormat().numAttributes(); j++) {
if (m_SelectCols.isInRange(j)) {
if (instance.attribute(j).isNumeric()
&& (!Utils.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
symbols.put("A", new Double(vals[j]));
symbols.put("MAX", new Double(m_attStats[j].numericStats.max));
symbols.put("MIN", new Double(m_attStats[j].numericStats.min));
symbols.put("MEAN", new Double(m_attStats[j].numericStats.mean));
symbols.put("SD", new Double(m_attStats[j].numericStats.stdDev));
symbols.put("COUNT", new Double(m_attStats[j].numericStats.count));
symbols.put("SUM", new Double(m_attStats[j].numericStats.sum));
symbols.put("SUMSQUARED", new Double(m_attStats[j].numericStats.sumSq));
vals[j] = eval(symbols);
if (Double.isNaN(vals[j]) || Double.isInfinite(vals[j])) {
System.err.println("WARNING:Error in Evaluation the Expression: missing value set");
vals[j] = Utils.missingValue();
}
}
}
}
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(instance.dataset());
push(inst);
}
@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);
}
}
}
}
/**
* Gets the results for the supplied train and test datasets. Now performs a deep copy of the
* classifier before it is built and evaluated (just in case the classifier is not initialized
* properly in buildClassifier()).
*
* @param train the training Instances.
* @param test the testing Instances.
* @return the results stored in an array. The objects stored in the array may be Strings,
* Doubles, or null (for the missing value).
* @throws Exception if a problem occurs while getting the results
*/
public Object[] getResult(Instances train, Instances test) throws Exception {
if (train.classAttribute().type() != Attribute.NUMERIC) {
throw new Exception("Class attribute is not numeric!");
}
if (m_Template == null) {
throw new Exception("No classifier has been specified");
}
ThreadMXBean thMonitor = ManagementFactory.getThreadMXBean();
boolean canMeasureCPUTime = thMonitor.isThreadCpuTimeSupported();
if (canMeasureCPUTime && !thMonitor.isThreadCpuTimeEnabled())
thMonitor.setThreadCpuTimeEnabled(true);
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
Object[] result = new Object[RESULT_SIZE + addm + m_numPluginStatistics];
long thID = Thread.currentThread().getId();
long CPUStartTime = -1,
trainCPUTimeElapsed = -1,
testCPUTimeElapsed = -1,
trainTimeStart,
trainTimeElapsed,
testTimeStart,
testTimeElapsed;
Evaluation eval = new Evaluation(train);
m_Classifier = AbstractClassifier.makeCopy(m_Template);
trainTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) CPUStartTime = thMonitor.getThreadUserTime(thID);
m_Classifier.buildClassifier(train);
if (canMeasureCPUTime) trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
testTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) CPUStartTime = thMonitor.getThreadUserTime(thID);
eval.evaluateModel(m_Classifier, test);
if (canMeasureCPUTime) testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
thMonitor = null;
m_result = eval.toSummaryString();
// The results stored are all per instance -- can be multiplied by the
// number of instances to get absolute numbers
int current = 0;
result[current++] = new Double(train.numInstances());
result[current++] = new Double(eval.numInstances());
result[current++] = new Double(eval.meanAbsoluteError());
result[current++] = new Double(eval.rootMeanSquaredError());
result[current++] = new Double(eval.relativeAbsoluteError());
result[current++] = new Double(eval.rootRelativeSquaredError());
result[current++] = new Double(eval.correlationCoefficient());
result[current++] = new Double(eval.SFPriorEntropy());
result[current++] = new Double(eval.SFSchemeEntropy());
result[current++] = new Double(eval.SFEntropyGain());
result[current++] = new Double(eval.SFMeanPriorEntropy());
result[current++] = new Double(eval.SFMeanSchemeEntropy());
result[current++] = new Double(eval.SFMeanEntropyGain());
// Timing stats
result[current++] = new Double(trainTimeElapsed / 1000.0);
result[current++] = new Double(testTimeElapsed / 1000.0);
if (canMeasureCPUTime) {
result[current++] = new Double((trainCPUTimeElapsed / 1000000.0) / 1000.0);
result[current++] = new Double((testCPUTimeElapsed / 1000000.0) / 1000.0);
} else {
result[current++] = new Double(Utils.missingValue());
result[current++] = new Double(Utils.missingValue());
}
// sizes
if (m_NoSizeDetermination) {
result[current++] = -1.0;
result[current++] = -1.0;
result[current++] = -1.0;
} else {
ByteArrayOutputStream bastream = new ByteArrayOutputStream();
ObjectOutputStream oostream = new ObjectOutputStream(bastream);
oostream.writeObject(m_Classifier);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(train);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(test);
result[current++] = new Double(bastream.size());
}
// Prediction interval statistics
result[current++] = new Double(eval.coverageOfTestCasesByPredictedRegions());
result[current++] = new Double(eval.sizeOfPredictedRegions());
if (m_Classifier instanceof Summarizable) {
result[current++] = ((Summarizable) m_Classifier).toSummaryString();
} else {
result[current++] = null;
}
for (int i = 0; i < addm; i++) {
if (m_doesProduce[i]) {
try {
double dv =
((AdditionalMeasureProducer) m_Classifier).getMeasure(m_AdditionalMeasures[i]);
if (!Utils.isMissingValue(dv)) {
Double value = new Double(dv);
result[current++] = value;
} else {
result[current++] = null;
}
} catch (Exception ex) {
System.err.println(ex);
}
} else {
result[current++] = null;
}
}
// get the actual metrics from the evaluation object
List<AbstractEvaluationMetric> metrics = eval.getPluginMetrics();
if (metrics != null) {
for (AbstractEvaluationMetric m : metrics) {
if (m.appliesToNumericClass()) {
List<String> statNames = m.getStatisticNames();
for (String s : statNames) {
result[current++] = new Double(m.getStatistic(s));
}
}
}
}
if (current != RESULT_SIZE + addm + m_numPluginStatistics) {
throw new Error("Results didn't fit RESULT_SIZE");
}
return result;
}
/**
* Classifies a given instance.
*
* @param instance the instance to be classified
* @return index of the predicted class
* @throws Exception if an error occurred during the prediction
*/
public double classifyInstance(Instance instance) throws Exception {
if (m_GroovyObject != null) return m_GroovyObject.classifyInstance(instance);
else return Utils.missingValue();
}
public static Instances retrieveInstances(InstanceQueryAdapter adapter, ResultSet rs)
throws Exception {
if (adapter.getDebug()) System.err.println("Getting metadata...");
ResultSetMetaData md = rs.getMetaData();
if (adapter.getDebug()) System.err.println("Completed getting metadata...");
// Determine structure of the instances
int numAttributes = md.getColumnCount();
int[] attributeTypes = new int[numAttributes];
Hashtable[] nominalIndexes = new Hashtable[numAttributes];
FastVector[] nominalStrings = new FastVector[numAttributes];
for (int i = 1; i <= numAttributes; i++) {
/* switch (md.getColumnType(i)) {
case Types.CHAR:
case Types.VARCHAR:
case Types.LONGVARCHAR:
case Types.BINARY:
case Types.VARBINARY:
case Types.LONGVARBINARY:*/
switch (adapter.translateDBColumnType(md.getColumnTypeName(i))) {
case STRING:
// System.err.println("String --> nominal");
attributeTypes[i - 1] = Attribute.NOMINAL;
nominalIndexes[i - 1] = new Hashtable();
nominalStrings[i - 1] = new FastVector();
break;
case TEXT:
// System.err.println("Text --> string");
attributeTypes[i - 1] = Attribute.STRING;
nominalIndexes[i - 1] = new Hashtable();
nominalStrings[i - 1] = new FastVector();
break;
case BOOL:
// System.err.println("boolean --> nominal");
attributeTypes[i - 1] = Attribute.NOMINAL;
nominalIndexes[i - 1] = new Hashtable();
nominalIndexes[i - 1].put("false", new Double(0));
nominalIndexes[i - 1].put("true", new Double(1));
nominalStrings[i - 1] = new FastVector();
nominalStrings[i - 1].addElement("false");
nominalStrings[i - 1].addElement("true");
break;
case DOUBLE:
// System.err.println("BigDecimal --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case BYTE:
// System.err.println("byte --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case SHORT:
// System.err.println("short --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case INTEGER:
// System.err.println("int --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case LONG:
// System.err.println("long --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case FLOAT:
// System.err.println("float --> numeric");
attributeTypes[i - 1] = Attribute.NUMERIC;
break;
case DATE:
attributeTypes[i - 1] = Attribute.DATE;
break;
case TIME:
attributeTypes[i - 1] = Attribute.DATE;
break;
default:
// System.err.println("Unknown column type");
attributeTypes[i - 1] = Attribute.STRING;
}
}
// For sqlite
// cache column names because the last while(rs.next()) { iteration for
// the tuples below will close the md object:
Vector<String> columnNames = new Vector<String>();
for (int i = 0; i < numAttributes; i++) {
columnNames.add(md.getColumnLabel(i + 1));
}
// Step through the tuples
if (adapter.getDebug()) System.err.println("Creating instances...");
FastVector instances = new FastVector();
int rowCount = 0;
while (rs.next()) {
if (rowCount % 100 == 0) {
if (adapter.getDebug()) {
System.err.print("read " + rowCount + " instances \r");
System.err.flush();
}
}
double[] vals = new double[numAttributes];
for (int i = 1; i <= numAttributes; i++) {
/*switch (md.getColumnType(i)) {
case Types.CHAR:
case Types.VARCHAR:
case Types.LONGVARCHAR:
case Types.BINARY:
case Types.VARBINARY:
case Types.LONGVARBINARY:*/
switch (adapter.translateDBColumnType(md.getColumnTypeName(i))) {
case STRING:
String str = rs.getString(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
Double index = (Double) nominalIndexes[i - 1].get(str);
if (index == null) {
index = new Double(nominalStrings[i - 1].size());
nominalIndexes[i - 1].put(str, index);
nominalStrings[i - 1].addElement(str);
}
vals[i - 1] = index.doubleValue();
}
break;
case TEXT:
String txt = rs.getString(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
Double index = (Double) nominalIndexes[i - 1].get(txt);
if (index == null) {
index = new Double(nominalStrings[i - 1].size());
nominalIndexes[i - 1].put(txt, index);
nominalStrings[i - 1].addElement(txt);
}
vals[i - 1] = index.doubleValue();
}
break;
case BOOL:
boolean boo = rs.getBoolean(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (boo ? 1.0 : 0.0);
}
break;
case DOUBLE:
// BigDecimal bd = rs.getBigDecimal(i, 4);
double dd = rs.getDouble(i);
// Use the column precision instead of 4?
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
// newInst.setValue(i - 1, bd.doubleValue());
vals[i - 1] = dd;
}
break;
case BYTE:
byte by = rs.getByte(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (double) by;
}
break;
case SHORT:
short sh = rs.getShort(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (double) sh;
}
break;
case INTEGER:
int in = rs.getInt(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (double) in;
}
break;
case LONG:
long lo = rs.getLong(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (double) lo;
}
break;
case FLOAT:
float fl = rs.getFloat(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
vals[i - 1] = (double) fl;
}
break;
case DATE:
Date date = rs.getDate(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
// TODO: Do a value check here.
vals[i - 1] = (double) date.getTime();
}
break;
case TIME:
Time time = rs.getTime(i);
if (rs.wasNull()) {
vals[i - 1] = Utils.missingValue();
} else {
// TODO: Do a value check here.
vals[i - 1] = (double) time.getTime();
}
break;
default:
vals[i - 1] = Utils.missingValue();
}
}
Instance newInst;
if (adapter.getSparseData()) {
newInst = new SparseInstance(1.0, vals);
} else {
newInst = new DenseInstance(1.0, vals);
}
instances.addElement(newInst);
rowCount++;
}
// disconnectFromDatabase(); (perhaps other queries might be made)
// Create the header and add the instances to the dataset
if (adapter.getDebug()) System.err.println("Creating header...");
FastVector attribInfo = new FastVector();
for (int i = 0; i < numAttributes; i++) {
/* Fix for databases that uppercase column names */
// String attribName = attributeCaseFix(md.getColumnName(i + 1));
String attribName = adapter.attributeCaseFix(columnNames.get(i));
switch (attributeTypes[i]) {
case Attribute.NOMINAL:
attribInfo.addElement(new Attribute(attribName, nominalStrings[i]));
break;
case Attribute.NUMERIC:
attribInfo.addElement(new Attribute(attribName));
break;
case Attribute.STRING:
Attribute att = new Attribute(attribName, (FastVector) null);
attribInfo.addElement(att);
for (int n = 0; n < nominalStrings[i].size(); n++) {
att.addStringValue((String) nominalStrings[i].elementAt(n));
}
break;
case Attribute.DATE:
attribInfo.addElement(new Attribute(attribName, (String) null));
break;
default:
throw new Exception("Unknown attribute type");
}
}
Instances result = new Instances("QueryResult", attribInfo, instances.size());
for (int i = 0; i < instances.size(); i++) {
result.add((Instance) instances.elementAt(i));
}
return result;
}
