/**
* Process a classifier's prediction for an instance and update a set of plotting instances and
* additional plotting info. m_PlotShape for nominal class datasets holds shape types (actual data
* points have automatic shape type assignment; classifier error data points have box shape type).
* For numeric class datasets, the actual data points are stored in m_PlotInstances and m_PlotSize
* stores the error (which is later converted to shape size values).
*
* @param toPredict the actual data point
* @param classifier the classifier
* @param eval the evaluation object to use for evaluating the classifier on the instance to
* predict
* @see #m_PlotShapes
* @see #m_PlotSizes
* @see #m_PlotInstances
*/
public void process(Instance toPredict, Classifier classifier, Evaluation eval) {
double pred;
double[] values;
int i;
try {
pred = eval.evaluateModelOnceAndRecordPrediction(classifier, toPredict);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
toPredict =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(toPredict);
}
if (!m_SaveForVisualization) return;
if (m_PlotInstances != null) {
values = new double[m_PlotInstances.numAttributes()];
for (i = 0; i < m_PlotInstances.numAttributes(); i++) {
if (i < toPredict.classIndex()) {
values[i] = toPredict.value(i);
} else if (i == toPredict.classIndex()) {
values[i] = pred;
values[i + 1] = toPredict.value(i);
i++;
} else {
values[i] = toPredict.value(i - 1);
}
}
m_PlotInstances.add(new DenseInstance(1.0, values));
if (toPredict.classAttribute().isNominal()) {
if (toPredict.isMissing(toPredict.classIndex()) || Utils.isMissingValue(pred)) {
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
} else if (pred != toPredict.classValue()) {
// set to default error point shape
m_PlotShapes.addElement(new Integer(Plot2D.ERROR_SHAPE));
} else {
// otherwise set to constant (automatically assigned) point shape
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
}
m_PlotSizes.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
} else {
// store the error (to be converted to a point size later)
Double errd = null;
if (!toPredict.isMissing(toPredict.classIndex()) && !Utils.isMissingValue(pred)) {
errd = new Double(pred - toPredict.classValue());
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
} else {
// missing shape if actual class not present or prediction is missing
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
}
m_PlotSizes.addElement(errd);
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Use <code> classifyInstance </code> from <code> OSDLCore </code> and assign probability one to
* the chosen label. The implementation is heavily based on the same method in the <code>
* Classifier </code> class.
*
* @param instance the instance to be classified
* @return an array containing a single '1' on the index that <code> classifyInstance </code>
* returns.
*/
public double[] distributionForInstance(Instance instance) {
// based on the code from the Classifier class
double[] dist = new double[instance.numClasses()];
int classification = 0;
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
try {
classification = (int) Math.round(classifyInstance(instance));
} catch (Exception e) {
System.out.println("There was a problem with classifyIntance");
System.out.println(e.getMessage());
e.printStackTrace();
}
if (Utils.isMissingValue(classification)) {
return dist;
}
dist[classification] = 1.0;
return dist;
case Attribute.NUMERIC:
try {
dist[0] = classifyInstance(instance);
} catch (Exception e) {
System.out.println("There was a problem with classifyIntance");
System.out.println(e.getMessage());
e.printStackTrace();
}
return dist;
default:
return dist;
}
}
protected void searchMedian(Instances instances) {
medians = new double[instances.numAttributes()];
imputations = new int[instances.numAttributes()];
for (int j = 0; j < instances.numAttributes(); ++j) {
int numPresentValues = 0;
if (instances.attribute(j).isNumeric()) {
double[] values = new double[instances.numInstances()];
for (int i = 0; i < instances.numInstances(); ++i) {
Instance current = instances.get(i);
if (Utils.isMissingValue(current.value(j)) == false) {
values[numPresentValues] = current.value(j);
numPresentValues += 1;
}
}
if (numPresentValues > 0) {
double[] goodValues = Arrays.copyOf(values, numPresentValues);
Median median = new Median();
medians[j] = median.evaluate(goodValues);
}
}
}
for (int j = 0; j < instances.numAttributes(); ++j) {
if (instances.attribute(j).isNumeric()) {
Conversion.log(
"OK",
"Impute Numeric",
"Attribute " + instances.attribute(j) + " - Median: " + medians[j]);
}
}
}
/**
* Gets the raw output from the classifier
*
* @return the raw output from the classifier
*/
public String getRawResultOutput() {
StringBuffer result = new StringBuffer();
if (m_Classifier == null) {
return "<null> classifier";
}
result.append(toString());
result.append("Classifier model: \n" + m_Classifier.toString() + '\n');
// append the performance statistics
if (m_result != null) {
result.append(m_result);
if (m_doesProduce != null) {
for (int i = 0; i < m_doesProduce.length; 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.append(m_AdditionalMeasures[i] + " : " + value + '\n');
} else {
result.append(m_AdditionalMeasures[i] + " : " + '?' + '\n');
}
} catch (Exception ex) {
System.err.println(ex);
}
}
}
}
}
return result.toString();
}
/**
* Convert an <code>Instance</code> to an array of values that matches the format of the mining
* schema. First maps raw attribute values and then applies rules for missing values, outliers
* etc.
*
* @param inst the <code>Instance</code> to convert
* @param miningSchema the mining schema incoming instance attributes
* @return an array of doubles that are values from the incoming Instances, correspond to the
* format of the mining schema and have had missing values, outliers etc. dealt with.
* @throws Exception if something goes wrong
*/
public double[] instanceToSchema(Instance inst, MiningSchema miningSchema) throws Exception {
Instances miningSchemaI = miningSchema.getMiningSchemaAsInstances();
// allocate enough space for both mining schema fields and any derived fields
double[] result = new double[miningSchema.getFieldsAsInstances().numAttributes()];
// Copy over the values
for (int i = 0; i < miningSchemaI.numAttributes(); i++) {
// if (miningSchemaI.attribute(i).isNumeric()) {
result[i] = inst.value(m_fieldsMap[i]);
if (miningSchemaI.attribute(i).isNominal() || miningSchemaI.attribute(i).isString()) {
// If not missing, look up the index of this incoming categorical value in
// the mining schema
if (!Utils.isMissingValue(inst.value(m_fieldsMap[i]))) {
int[] valueMap = m_nominalValueMaps[i];
int index = valueMap[(int) inst.value(m_fieldsMap[i])];
String incomingAttValue =
inst.attribute(m_fieldsMap[i]).value((int) inst.value(m_fieldsMap[i]));
/*int index = miningSchemaI.attribute(i).indexOfValue(incomingAttValue); */
if (index >= 0) {
result[i] = index;
} else {
// set this to "unknown" (-1) for nominal valued attributes
result[i] = UNKNOWN_NOMINAL_VALUE;
String warningString =
"[MappingInfo] WARNING: Can't match nominal value " + incomingAttValue;
if (m_log != null) {
m_log.logMessage(warningString);
} else {
System.err.println(warningString);
}
}
}
}
}
// Now deal with missing values and outliers...
miningSchema.applyMissingAndOutlierTreatments(result);
// printInst(result);
// now fill in any derived values
ArrayList<DerivedFieldMetaInfo> derivedFields = miningSchema.getDerivedFields();
for (int i = 0; i < derivedFields.size(); i++) {
DerivedFieldMetaInfo temp = derivedFields.get(i);
// System.err.println("Applying : " + temp);
double r = temp.getDerivedValue(result);
result[i + miningSchemaI.numAttributes()] = r;
}
/*System.err.print("==> ");
for (int i = 0; i < result.length; i++) {
System.err.print(" " + result[i]);
}
System.err.println();*/
return result;
}
protected void imputeMedian(Instances instances) {
Attribute indicator = instances.attribute(ATTNAME_INDICATOR);
for (int i = 0; i < instances.numInstances(); ++i) {
Instance current = instances.get(i);
current.setValue(indicator, 0.0); // 0.0 means "false"
for (int j = 0; j < instances.numAttributes(); ++j) {
if (instances.attribute(j).isNumeric() == false) {
continue;
}
if (Utils.isMissingValue(current.value(j))) {
current.setValue(j, medians[j]);
current.setValue(indicator, 1.0);
imputations[j] += 1;
}
}
}
}
/**
* Predicts the class memberships for a given instance. If an instance is unclassified, the
* returned array elements must be all zero. If the class is numeric, the array must consist of
* only one element, which contains the predicted value. Note that a classifier MUST implement
* either this or classifyInstance().
*
* @param instance the instance to be classified
* @return an array containing the estimated membership probabilities of the test instance in each
* class or the numeric prediction
* @exception Exception if distribution could not be computed successfully
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] dist = new double[instance.numClasses()];
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double classification = classifyInstance(instance);
if (Utils.isMissingValue(classification)) {
return dist;
} else {
dist[(int) classification] = 1.0;
}
return dist;
case Attribute.NUMERIC:
case Attribute.DATE:
dist[0] = classifyInstance(instance);
return dist;
default:
return dist;
}
}
/**
* Outputs a tree at a certain level.
*
* @param level the level at which the tree is to be printed
* @return the tree as string at the given level
*/
private String toString(int level) {
StringBuffer text = new StringBuffer();
if (m_Attribute == null) {
if (Utils.isMissingValue(m_ClassValue)) {
text.append(": null");
} else {
text.append(": " + m_ClassAttribute.value((int) m_ClassValue));
}
} else {
for (int j = 0; j < m_Attribute.numValues(); j++) {
text.append("\n");
for (int i = 0; i < level; i++) {
text.append("| ");
}
text.append(m_Attribute.name() + " = " + m_Attribute.value(j));
text.append(m_Successors[j].toString(level + 1));
}
}
return text.toString();
}
/**
* Signify that this batch of input to the filter is finished. If the filter requires all
* instances prior to filtering, output() may now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @throws Exception if an error occurs
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null)
throw new IllegalStateException("No input instance format defined");
if (m_MinArray == null) {
Instances input = getInputFormat();
// Compute minimums and maximums
m_MinArray = new double[input.numAttributes()];
m_MaxArray = new double[input.numAttributes()];
for (int i = 0; i < input.numAttributes(); i++) m_MinArray[i] = Double.NaN;
for (int j = 0; j < input.numInstances(); j++) {
double[] value = input.instance(j).toDoubleArray();
for (int i = 0; i < input.numAttributes(); i++) {
if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
if (!Utils.isMissingValue(value[i])) {
if (Double.isNaN(m_MinArray[i])) {
m_MinArray[i] = m_MaxArray[i] = value[i];
} else {
if (value[i] < m_MinArray[i]) m_MinArray[i] = value[i];
if (value[i] > m_MaxArray[i]) m_MaxArray[i] = value[i];
}
}
}
}
}
// Convert pending input instances
for (int i = 0; i < input.numInstances(); i++) convertInstance(input.instance(i));
}
// Free memory
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* 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 conversion fails
*/
protected void convertInstance(Instance instance) throws Exception {
Instance inst = null;
if (instance instanceof SparseInstance) {
double[] newVals = new double[instance.numAttributes()];
int[] newIndices = new int[instance.numAttributes()];
double[] vals = instance.toDoubleArray();
int ind = 0;
for (int j = 0; j < instance.numAttributes(); j++) {
double value;
if (instance.attribute(j).isNumeric()
&& (!Utils.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
if (Double.isNaN(m_MinArray[j]) || (m_MaxArray[j] == m_MinArray[j])) {
value = 0;
} else {
value =
(vals[j] - m_MinArray[j]) / (m_MaxArray[j] - m_MinArray[j]) * m_Scale
+ m_Translation;
if (Double.isNaN(value)) {
throw new Exception(
"A NaN value was generated "
+ "while normalizing "
+ instance.attribute(j).name());
}
}
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 (instance.attribute(j).isNumeric()
&& (!Utils.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
if (Double.isNaN(m_MinArray[j]) || (m_MaxArray[j] == m_MinArray[j])) {
vals[j] = 0;
} else {
vals[j] =
(vals[j] - m_MinArray[j]) / (m_MaxArray[j] - m_MinArray[j]) * m_Scale
+ m_Translation;
if (Double.isNaN(vals[j])) {
throw new Exception(
"A NaN value was generated "
+ "while normalizing "
+ instance.attribute(j).name());
}
}
}
}
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(instance.dataset());
push(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);
}
/**
* Evaluates a feature subset by cross validation
*
* @param feature_set the subset to be evaluated
* @param num_atts the number of attributes in the subset
* @return the estimated accuracy
* @throws Exception if subset can't be evaluated
*/
protected double estimatePerformance(BitSet feature_set, int num_atts) throws Exception {
m_evaluation = new Evaluation(m_theInstances);
int i;
int[] fs = new int[num_atts];
double[] instA = new double[num_atts];
int classI = m_theInstances.classIndex();
int index = 0;
for (i = 0; i < m_numAttributes; i++) {
if (feature_set.get(i)) {
fs[index++] = i;
}
}
// create new hash table
m_entries = new Hashtable((int) (m_theInstances.numInstances() * 1.5));
// insert instances into the hash table
for (i = 0; i < m_numInstances; i++) {
Instance inst = m_theInstances.instance(i);
for (int j = 0; j < fs.length; j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
insertIntoTable(inst, instA);
}
if (m_CVFolds == 1) {
// calculate leave one out error
for (i = 0; i < m_numInstances; i++) {
Instance inst = m_theInstances.instance(i);
for (int j = 0; j < fs.length; j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
evaluateInstanceLeaveOneOut(inst, instA);
}
} else {
m_theInstances.randomize(m_rr);
m_theInstances.stratify(m_CVFolds);
// calculate 10 fold cross validation error
for (i = 0; i < m_CVFolds; i++) {
Instances insts = m_theInstances.testCV(m_CVFolds, i);
evaluateFoldCV(insts, fs);
}
}
switch (m_evaluationMeasure) {
case EVAL_DEFAULT:
if (m_classIsNominal) {
return m_evaluation.pctCorrect();
}
return -m_evaluation.rootMeanSquaredError();
case EVAL_ACCURACY:
return m_evaluation.pctCorrect();
case EVAL_RMSE:
return -m_evaluation.rootMeanSquaredError();
case EVAL_MAE:
return -m_evaluation.meanAbsoluteError();
case EVAL_AUC:
double[] classPriors = m_evaluation.getClassPriors();
Utils.normalize(classPriors);
double weightedAUC = 0;
for (i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
double tempAUC = m_evaluation.areaUnderROC(i);
if (!Utils.isMissingValue(tempAUC)) {
weightedAUC += (classPriors[i] * tempAUC);
} else {
System.err.println("Undefined AUC!!");
}
}
return weightedAUC;
}
// shouldn't get here
return 0.0;
}
/**
* 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;
}
