@Override
public final void doWork() throws OperatorException {
ExampleSet inputExampleSet = exampleSetInput.getData(ExampleSet.class);
ExampleSet applySet = null;
// check for needed copy of original exampleset
if (originalOutput.isConnected() && writesIntoExistingData()) {
int type = DataRowFactory.TYPE_DOUBLE_ARRAY;
if (inputExampleSet.getExampleTable() instanceof MemoryExampleTable) {
DataRowReader dataRowReader = inputExampleSet.getExampleTable().getDataRowReader();
if (dataRowReader.hasNext()) {
type = dataRowReader.next().getType();
}
}
// check if type is supported to be copied
if (type >= 0) {
applySet = MaterializeDataInMemory.materializeExampleSet(inputExampleSet, type);
}
}
if (applySet == null) applySet = (ExampleSet) inputExampleSet.clone();
// we apply on the materialized data, because writing can't take place in views anyway.
ExampleSet result = apply(applySet);
originalOutput.deliver(inputExampleSet);
exampleSetOutput.deliver(result);
}
/**
* Generates all new attributes and updates the ExampleTable. Returns a list of Attributes for the
* newly generated attributes.
*
* @param exampleTable the source example table
* @param generatorList List of FeatureGenerators
* @return A list of Attributes
*/
public static List<Attribute> generateAll(
ExampleTable exampleTable, Collection<FeatureGenerator> generatorList)
throws GenerationException {
// LogService.getGlobal().log("Starting feature generation with " + generatorList.size() + "
// generators.", LogService.STATUS);
LogService.getRoot()
.log(
Level.FINE,
"com.rapidminer.generator.FeatureGenerator.starting_feature_generation",
generatorList.size());
Iterator<FeatureGenerator> gi = generatorList.iterator();
while (gi.hasNext()) gi.next().setExampleTable(exampleTable);
// for performance reasons convert the list to an array
FeatureGenerator[] generators = new FeatureGenerator[generatorList.size()];
generatorList.toArray(generators);
List<Attribute> newAttributeList = newAttributes(generators, exampleTable);
// add the attributes to the example table and ensure length of the
// DataRows
exampleTable.addAttributes(newAttributeList);
// LogService.getGlobal().log("Generator list: " + generatorList, LogService.STATUS);
LogService.getRoot()
.log(Level.FINE, "com.rapidminer.generator.FeatureGenerator.generator_list", generatorList);
// LogService.getGlobal().log("Input set has " + exampleTable.getAttributeCount() + " features,
// " + exampleTable.size() + " examples.", LogService.STATUS);
LogService.getRoot()
.log(
Level.FINE,
"com.rapidminer.generator.FeatureGenerator.input_has_feature_count_and_example_count",
new Object[] {exampleTable.getAttributeCount(), exampleTable.size()});
// generate the attribute values:
DataRowReader reader = exampleTable.getDataRowReader();
while (reader.hasNext()) {
DataRow dataRow = reader.next();
for (int j = 0; j < generators.length; j++) {
generators[j].generate(dataRow);
}
}
// LogService.getGlobal().log("Finished feature generation.", LogService.STATUS);
LogService.getRoot()
.log(Level.FINE, "com.rapidminer.generator.FeatureGenerator.finished_feature_generation");
// LogService.getGlobal().log("Generated set has " + exampleTable.getAttributeCount() + "
// features, " + exampleTable.size() + " examples.", LogService.STATUS);
LogService.getRoot()
.log(
Level.FINE,
"com.rapidminer.generator.FeatureGenerator.generated_set_has_feature_count_and_example_count",
new Object[] {exampleTable.getAttributeCount(), exampleTable.size()});
return newAttributeList;
}