public void checkConstraints(ExampleTable et) {
super.checkConstraints(et);
RelationalAttribute relA = null;
for (int i = 0; i < et.getNumberOfAttributes(); i++)
if (et.getAttribute(i).getTableIndex() == this.getAttributeIndexes()[0]) {
relA = (RelationalAttribute) et.getAttribute(i);
break;
}
// sparse matrix inner relational attributes
if (relA.getInnerAttributeCount() != 2)
throw new IllegalArgumentException(
"sparse matrix attribute "
+ this.getName()
+ " must wrap relational attribute with exactly two inner attributes");
else if (!Ontology.ATTRIBUTE_VALUE_TYPE.isA(
relA.getInnerAttributeAt(0).getValueType(), Ontology.NUMERICAL))
throw new IllegalArgumentException(
"sparse matrix attribute "
+ this.getName()
+ " must wrap relational attribute which inner first attribute serves as key and therefore must be numerical");
}
/**
* 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;
}
