/**
* Builds a new LinearRegression without the 'bad' data found by buildWeight
*
* @throws Exception if building fails
*/
private void buildRLSRegression() throws Exception {
buildWeight();
m_RLSData = new Instances(m_Data);
int x = 0;
int y = 0;
int n = m_RLSData.numInstances();
while (y < n) {
if (m_weight[x] == 0) {
m_RLSData.delete(y);
n = m_RLSData.numInstances();
y--;
}
x++;
y++;
}
if (m_RLSData.numInstances() == 0) {
System.err.println("rls regression unbuilt");
m_ls = m_currentRegression;
} else {
m_ls = new LinearRegression();
m_ls.setOptions(new String[] {"-S", "1"});
m_ls.buildClassifier(m_RLSData);
m_currentRegression = m_ls;
}
}
/**
* Finds residuals (squared) for the current regression.
*
* @throws Exception if an error occurs
*/
private void findResiduals() throws Exception {
m_SSR = 0;
m_Residuals = new double[m_Data.numInstances()];
for (int i = 0; i < m_Data.numInstances(); i++) {
m_Residuals[i] = m_currentRegression.classifyInstance(m_Data.instance(i));
m_Residuals[i] -= m_Data.instance(i).value(m_Data.classAttribute());
m_Residuals[i] *= m_Residuals[i];
m_SSR += m_Residuals[i];
}
}
/**
* Gets the number of samples to use.
*
* @throws Exception if an error occurs
*/
private void getSamples() throws Exception {
int stuf[] = new int[] {500, 50, 22, 17, 15, 14};
if (m_samplesize < 7) {
if (m_Data.numInstances() < stuf[m_samplesize - 1])
m_samples = combinations(m_Data.numInstances(), m_samplesize);
else m_samples = m_samplesize * 500;
} else m_samples = 3000;
if (m_debug) {
System.out.println("m_samplesize: " + m_samplesize);
System.out.println("m_samples: " + m_samples);
System.out.println("m_randomseed: " + m_randomseed);
}
}
/**
* Creates a new <code>TestSetEvent</code>
*
* @param source the source of the event
* @param testSet the test instances
*/
public TestSetEvent(Object source, Instances testSet) {
super(source);
m_testSet = testSet;
if (m_testSet != null && m_testSet.numInstances() == 0) {
m_structureOnly = true;
}
}
/**
* Calculates the performance stats for the desired class and return results as a set of
* Instances.
*
* @param predictions the predictions to base the curve on
* @param classIndex index of the class of interest.
* @return datapoints as a set of instances.
*/
public Instances getCurve(FastVector predictions, int classIndex) {
if ((predictions.size() == 0)
|| (((NominalPrediction) predictions.elementAt(0)).distribution().length <= classIndex)) {
return null;
}
ThresholdCurve tc = new ThresholdCurve();
Instances threshInst = tc.getCurve(predictions, classIndex);
Instances insts = makeHeader();
int fpind = threshInst.attribute(ThresholdCurve.FP_RATE_NAME).index();
int tpind = threshInst.attribute(ThresholdCurve.TP_RATE_NAME).index();
int threshind = threshInst.attribute(ThresholdCurve.THRESHOLD_NAME).index();
double[] vals;
double fpval, tpval, thresh;
for (int i = 0; i < threshInst.numInstances(); i++) {
fpval = threshInst.instance(i).value(fpind);
tpval = threshInst.instance(i).value(tpind);
thresh = threshInst.instance(i).value(threshind);
vals = new double[3];
vals[0] = 0;
vals[1] = fpval;
vals[2] = thresh;
insts.add(new Instance(1.0, vals));
vals = new double[3];
vals[0] = 1;
vals[1] = 1.0 - tpval;
vals[2] = thresh;
insts.add(new Instance(1.0, vals));
}
return insts;
}
/**
* 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
* @exception Exception if an error occurs
* @exception 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_Means == null) {
Instances input = getInputFormat();
m_Means = new double[input.numAttributes()];
m_StdDevs = new double[input.numAttributes()];
for (int i = 0; i < input.numAttributes(); i++) {
if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
m_Means[i] = input.meanOrMode(i);
m_StdDevs[i] = Math.sqrt(input.variance(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);
}
/**
* Builds a weight function removing instances with an abnormally high scaled residual
*
* @throws Exception if weight building fails
*/
private void buildWeight() throws Exception {
findResiduals();
m_scalefactor =
1.4826
* (1 + 5 / (m_Data.numInstances() - m_Data.numAttributes()))
* Math.sqrt(m_bestMedian);
m_weight = new double[m_Residuals.length];
for (int i = 0; i < m_Residuals.length; i++)
m_weight[i] = ((Math.sqrt(m_Residuals[i]) / m_scalefactor < 2.5) ? 1.0 : 0.0);
}
/**
* Returns a string suitable for passing to RemoveRange consisting of m_samplesize indices.
*
* @param data dataset from which to take indicese
* @return string of indices suitable for passing to RemoveRange
*/
private String selectIndices(Instances data) {
StringBuffer text = new StringBuffer();
for (int i = 0, x = 0; i < m_samplesize; i++) {
do {
x = (int) (m_random.nextDouble() * data.numInstances());
} while (x == 0);
text.append(Integer.toString(x));
if (i < m_samplesize - 1) text.append(",");
else text.append("\n");
}
return text.toString();
}
/**
* Converts the header info of the given set of instances into a set of item sets (singletons).
* The ordering of values in the header file determines the lexicographic order.
*
* @param instances the set of instances whose header info is to be used
* @return a set of item sets, each containing a single item
* @exception Exception if singletons can't be generated successfully
*/
public static FastVector singletons(Instances instances) throws Exception {
FastVector setOfItemSets = new FastVector();
ItemSet current;
for (int i = 0; i < instances.numAttributes(); i++) {
if (instances.attribute(i).isNumeric())
throw new Exception("Can't handle numeric attributes!");
for (int j = 0; j < instances.attribute(i).numValues(); j++) {
current = new AprioriItemSet(instances.numInstances());
current.m_items = new int[instances.numAttributes()];
for (int k = 0; k < instances.numAttributes(); k++) current.m_items[k] = -1;
current.m_items[i] = j;
setOfItemSets.addElement(current);
}
}
return setOfItemSets;
}
