Java Code Examples for weka.classifiers.lazy.IBk#buildClassifier()
The following examples show how to use
weka.classifiers.lazy.IBk#buildClassifier() .
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Example 1
| Source File: kNN.java From tsml with GNU General Public License v3.0 | 5 votes |
|
public static void test1NNvsIB1(boolean norm){
System.out.println("FIRST BASIC SANITY TEST FOR THIS WRAPPER");
System.out.print("Compare 1-NN with IB1, normalisation turned");
String str=norm?" on":" off";
System.out.println(str);
System.out.println("Compare on the UCI data sets");
System.out.print("If normalisation is off, then there may be differences");
kNN knn = new kNN(1);
IBk ib1=new IBk(1);
knn.normalise(norm);
int diff=0;
DecimalFormat df = new DecimalFormat("####.###");
for(String s:DatasetLists.uciFileNames){
Instances train=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"/"+s+"-train");
Instances test=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"/"+s+"-test");
try{
knn.buildClassifier(train);
// ib1.buildClassifier(train);
ib1.buildClassifier(train);
double a1=ClassifierTools.accuracy(test, knn);
double a2=ClassifierTools.accuracy(test, ib1);
if(a1!=a2){
diff++;
System.out.println(s+": 1-NN ="+df.format(a1)+" ib1="+df.format(a2));
}
}catch(Exception e){
System.out.println(" Exception builing a classifier");
System.exit(0);
}
}
System.out.println("Total problems ="+DatasetLists.uciFileNames.length+" different on "+diff);
}
Example 2
| Source File: kNN.java From tsml with GNU General Public License v3.0 | 5 votes |
|
public static void testkNNvsIBk(boolean norm, boolean crossValidate){
System.out.println("FIRST BASIC SANITY TEST FOR THIS WRAPPER");
System.out.print("Compare 1-NN with IB1, normalisation turned");
String str=norm?" on":" off";
System.out.println(str);
System.out.print("Cross validation turned");
str=crossValidate?" on":" off";
System.out.println(str);
System.out.println("Compare on the UCI data sets");
System.out.print("If normalisation is off, then there may be differences");
kNN knn = new kNN(100);
IBk ibk=new IBk(100);
knn.normalise(norm);
knn.setCrossValidate(crossValidate);
ibk.setCrossValidate(crossValidate);
int diff=0;
DecimalFormat df = new DecimalFormat("####.###");
for(String s:DatasetLists.uciFileNames){
Instances train=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"\\"+s+"-train");
Instances test=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"\\"+s+"-test");
try{
knn.buildClassifier(train);
// ib1.buildClassifier(train);
ibk.buildClassifier(train);
double a1=ClassifierTools.accuracy(test, knn);
double a2=ClassifierTools.accuracy(test, ibk);
if(a1!=a2){
diff++;
System.out.println(s+": 1-NN ="+df.format(a1)+" ibk="+df.format(a2));
}
}catch(Exception e){
System.out.println(" Exception builing a classifier");
System.exit(0);
}
}
System.out.println("Total problems ="+DatasetLists.uciFileNames.length+" different on "+diff);
}
Example 3
| Source File: EvaluationUtils.java From AILibs with GNU Affero General Public License v3.0 | 5 votes |
|
public static double performEnsemble(Instances instances) throws Exception {
List<Instances> subsample = WekaUtil.getStratifiedSplit(instances, 42, .05f);
instances = subsample.get(0);
/* Relief */
ReliefFAttributeEval relief = new ReliefFAttributeEval();
relief.buildEvaluator(instances);
double attEvalSum = 0;
for (int i = 0; i < instances.numAttributes() - 1; i++) {
attEvalSum += relief.evaluateAttribute(i);
}
attEvalSum /= instances.numAttributes();
/* Variance */
double varianceMean = 0;
int totalNumericCount = 0;
for (int i = 0; i < instances.numAttributes() - 1; i++) {
if (instances.attribute(i).isNumeric()) {
instances.attributeStats(i).numericStats.calculateDerived();
varianceMean += Math.pow(instances.attributeStats(i).numericStats.stdDev, 2);
totalNumericCount++;
}
}
varianceMean /= (totalNumericCount != 0 ? totalNumericCount : 1);
/* KNN */
List<Instances> split = WekaUtil.getStratifiedSplit(instances, 42, .7f);
IBk knn = new IBk(10);
knn.buildClassifier(split.get(0));
Evaluation eval = new Evaluation(split.get(0));
eval.evaluateModel(knn, split.get(1));
double knnResult = eval.pctCorrect() / 100d;
return 1 - (0.33 * attEvalSum + 0.33 * knnResult + 0.33 * varianceMean);
}
Example 4
| Source File: EnsembleEvaluatorTest.java From AILibs with GNU Affero General Public License v3.0 | 5 votes |
|
@Test
public void knnEvaluatorTest() throws Exception {
logger.info("Starting knn evaluation test...");
/* load dataset and create a train-test-split */
OpenmlConnector connector = new OpenmlConnector();
DataSetDescription ds = connector.dataGet(DataSetUtils.SEGMENT_ID);
File file = ds.getDataset(DataSetUtils.API_KEY);
Instances data = new Instances(new BufferedReader(new FileReader(file)));
data.setClassIndex(data.numAttributes() - 1);
List<Instances> split = WekaUtil.getStratifiedSplit(data, 42, .05f);
Instances insts = split.get(0);
List<Instances> split2 = WekaUtil.getStratifiedSplit(insts, 42, .7f);
long timeStart = System.currentTimeMillis();
IBk knn = new IBk(10);
knn.buildClassifier(split2.get(0));
long timeStartEval = System.currentTimeMillis();
Evaluation eval = new Evaluation(split2.get(0));
eval.evaluateModel(knn, split2.get(1));
logger.debug("Pct correct: " + eval.pctCorrect());
Assert.assertTrue(eval.pctCorrect() > 0);
long timeTaken = System.currentTimeMillis() - timeStart;
long timeTakenEval = System.currentTimeMillis() - timeStartEval;
logger.debug("KNN took " + (timeTaken / 1000) + " s.");
logger.debug("KNN eval took " + (timeTakenEval / 1000) + " s.");
}
Example 5
| Source File: TestIBk.java From Java-Data-Analysis with MIT License | 4 votes |
|
public static void main(String[] args) throws Exception {
DataSource source = new DataSource("data/AnonFruit.arff");
Instances instances = source.getDataSet();
instances.setClassIndex(3); // target attribute: (Sweet)
IBk ibk = new IBk();
ibk.buildClassifier(instances);
for (Instance instance : instances) {
double prediction = ibk.classifyInstance(instance);
System.out.printf("%4.0f%4.0f%n",
instance.classValue(), prediction);
}
}
