@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);
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
String attributeName = getParameterAsString(PARAMETER_ATTRIBUTE_NAME);
Long offset = getParameterAsLong(PARMETER_TIME_OFFSET);
Attribute numericalAttribute = exampleSet.getAttributes().get(attributeName);
if (numericalAttribute == null) {
throw new UserError(this, 111, attributeName);
}
Attribute newAttribute = AttributeFactory.createAttribute(Ontology.DATE_TIME);
exampleSet.getExampleTable().addAttribute(newAttribute);
exampleSet.getAttributes().addRegular(newAttribute);
for (Example example : exampleSet) {
double value = example.getValue(numericalAttribute);
if (Double.isNaN(value)) {
example.setValue(newAttribute, value);
} else {
value += offset;
example.setValue(newAttribute, value);
}
}
if (!getParameterAsBoolean(PARAMETER_KEEP_OLD_ATTRIBUTE)) {
AttributeRole oldRole = exampleSet.getAttributes().getRole(numericalAttribute);
exampleSet.getAttributes().remove(numericalAttribute);
newAttribute.setName(attributeName);
exampleSet.getAttributes().setSpecialAttribute(newAttribute, oldRole.getSpecialName());
} else {
newAttribute.setName(attributeName + "_AS_DATE");
}
return exampleSet;
}
@Override
public ExampleSet applyOnFiltered(ExampleSet exampleSet) throws OperatorException {
boolean round = getParameterAsBoolean(PARAMETER_ROUND);
List<Attribute> newAttributes = new LinkedList<Attribute>();
Iterator<Attribute> a = exampleSet.getAttributes().iterator();
while (a.hasNext()) {
Attribute attribute = a.next();
if ((Ontology.ATTRIBUTE_VALUE_TYPE.isA(attribute.getValueType(), Ontology.NUMERICAL))
&& (!Ontology.ATTRIBUTE_VALUE_TYPE.isA(attribute.getValueType(), Ontology.INTEGER))) {
Attribute newAttribute =
AttributeFactory.createAttribute(attribute.getName(), Ontology.INTEGER);
newAttributes.add(newAttribute);
exampleSet.getExampleTable().addAttribute(newAttribute);
for (Example example : exampleSet) {
double originalValue = example.getValue(attribute);
if (Double.isNaN(originalValue)) {
example.setValue(newAttribute, Double.NaN);
} else {
long newValue = round ? Math.round(originalValue) : (long) originalValue;
example.setValue(newAttribute, newValue);
}
}
a.remove();
}
}
for (Attribute attribute : newAttributes) exampleSet.getAttributes().addRegular(attribute);
return exampleSet;
}
@Override
public ClusterModel generateClusterModel(ExampleSet exampleSet) throws OperatorException {
// checking and creating ids if necessary
Tools.checkAndCreateIds(exampleSet);
// generating assignment
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
int clusterAssignments[] = new int[exampleSet.size()];
int k = getParameterAsInt(PARAMETER_NUMBER_OF_CLUSTERS);
for (int i = 0; i < exampleSet.size(); i++) {
clusterAssignments[i] = random.nextInt(k);
}
ClusterModel model =
new ClusterModel(
exampleSet,
k,
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_ADD_AS_LABEL),
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_REMOVE_UNLABELED));
model.setClusterAssignments(clusterAssignments, exampleSet);
// generating cluster attribute
if (addsClusterAttribute()) {
Attribute cluster = AttributeFactory.createAttribute("cluster", Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
exampleSet.getAttributes().setCluster(cluster);
int i = 0;
for (Example example : exampleSet) {
example.setValue(cluster, "cluster_" + clusterAssignments[i]);
i++;
}
}
return model;
}
/**
* Iterates over all models and returns the class with maximum likelihood.
*
* @param origExampleSet the set of examples to be classified
*/
@Override
public ExampleSet performPrediction(ExampleSet origExampleSet, Attribute predictedLabel)
throws OperatorException {
final String attributePrefix = "AdaBoostModelPrediction";
final int numLabels = predictedLabel.getMapping().size();
final Attribute[] specialAttributes = new Attribute[numLabels];
OperatorProgress progress = null;
if (getShowProgress() && getOperator() != null && getOperator().getProgress() != null) {
progress = getOperator().getProgress();
progress.setTotal(100);
}
for (int i = 0; i < numLabels; i++) {
specialAttributes[i] =
com.rapidminer.example.Tools.createSpecialAttribute(
origExampleSet, attributePrefix + i, Ontology.NUMERICAL);
if (progress != null) {
progress.setCompleted((int) (25.0 * (i + 1) / numLabels));
}
}
Iterator<Example> reader = origExampleSet.iterator();
int progressCounter = 0;
while (reader.hasNext()) {
Example example = reader.next();
for (int i = 0; i < specialAttributes.length; i++) {
example.setValue(specialAttributes[i], 0);
}
if (progress != null && ++progressCounter % OPERATOR_PROGRESS_STEPS == 0) {
progress.setCompleted((int) (25.0 * progressCounter / origExampleSet.size()) + 25);
}
}
reader = origExampleSet.iterator();
for (int modelNr = 0; modelNr < this.getNumberOfModels(); modelNr++) {
Model model = this.getModel(modelNr);
ExampleSet exampleSet = (ExampleSet) origExampleSet.clone();
exampleSet = model.apply(exampleSet);
this.updateEstimates(exampleSet, modelNr, specialAttributes);
PredictionModel.removePredictedLabel(exampleSet);
if (progress != null) {
progress.setCompleted((int) (25.0 * (modelNr + 1) / this.getNumberOfModels()) + 50);
}
}
// Turn prediction weights into confidences and a crisp predcition:
this.evaluateSpecialAttributes(origExampleSet, specialAttributes);
// Clean up attributes:
for (int i = 0; i < numLabels; i++) {
origExampleSet.getAttributes().remove(specialAttributes[i]);
origExampleSet.getExampleTable().removeAttribute(specialAttributes[i]);
if (progress != null) {
progress.setCompleted((int) (25.0 * (i + 1) / numLabels) + 75);
}
}
return origExampleSet;
}
/**
* Helper method replacing <code>Model.createPredictedLabel(ExampleSet)</code> in order to lower
* memory consumption.
*/
private static void createOrReplacePredictedLabelFor(ExampleSet exampleSet, Model model) {
Attribute predictedLabel = exampleSet.getAttributes().getPredictedLabel();
if (predictedLabel != null) { // remove old predicted label
exampleSet.getAttributes().remove(predictedLabel);
exampleSet.getExampleTable().removeAttribute(predictedLabel);
}
// model.createPredictedLabel(exampleSet); // not longer necessary since
// label creation is done by model.apply(...).
}
private void restoreOldWeights(ExampleSet exampleSet) {
if (this.oldWeights != null) { // need to reset weights
Iterator<Example> reader = exampleSet.iterator();
int i = 0;
while (reader.hasNext() && i < this.oldWeights.length) {
reader.next().setWeight(this.oldWeights[i++]);
}
} else { // need to delete the weights attribute
Attribute weight = exampleSet.getAttributes().getWeight();
exampleSet.getAttributes().remove(weight);
exampleSet.getExampleTable().removeAttribute(weight);
}
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
int size = exampleSet.size();
// cannot bootstrap without any examples
if (size < 1) {
throw new UserError(this, 117);
}
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
switch (getParameterAsInt(PARAMETER_SAMPLE)) {
case SAMPLE_ABSOLUTE:
size = getParameterAsInt(PARAMETER_SAMPLE_SIZE);
break;
case SAMPLE_RELATIVE:
size = (int) Math.round(exampleSet.size() * getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
break;
}
int[] mapping = null;
if (getParameterAsBoolean(PARAMETER_USE_WEIGHTS)
&& exampleSet.getAttributes().getWeight() != null) {
mapping = MappedExampleSet.createWeightedBootstrappingMapping(exampleSet, size, random);
} else {
mapping = MappedExampleSet.createBootstrappingMapping(exampleSet, size, random);
}
// create and materialize example set
ExampleSet mappedExampleSet = new MappedExampleSet(exampleSet, mapping, true);
if (getCompatibilityLevel().isAbove(VERSION_6_4_0)) {
int type = DataRowFactory.TYPE_DOUBLE_ARRAY;
if (exampleSet.size() > 0) {
type = exampleSet.getExampleTable().getDataRow(0).getType();
}
mappedExampleSet = MaterializeDataInMemory.materializeExampleSet(mappedExampleSet, type);
}
return mappedExampleSet;
}
@Override
public ExampleSet applyOnData(ExampleSet exampleSet) throws OperatorException {
Attributes attributes = exampleSet.getAttributes();
// constructing new attributes with generic names, holding old ones, if old type wasn't real
Attribute[] oldAttributes = new Attribute[attributes.size()];
int i = 0;
for (Attribute attribute : attributes) {
oldAttributes[i] = attribute;
i++;
}
Attribute[] newAttributes = new Attribute[attributes.size()];
for (i = 0; i < newAttributes.length; i++) {
newAttributes[i] = oldAttributes[i];
if (oldAttributes[i].isNumerical())
if (!Ontology.ATTRIBUTE_VALUE_TYPE.isA(oldAttributes[i].getValueType(), Ontology.REAL)) {
newAttributes[i] = AttributeFactory.createAttribute(Ontology.REAL);
exampleSet.getExampleTable().addAttribute(newAttributes[i]);
attributes.addRegular(newAttributes[i]);
}
}
// applying on data
applyOnData(exampleSet, oldAttributes, newAttributes);
// removing old attributes and change new attributes name to old ones if needed
for (i = 0; i < oldAttributes.length; i++) {
attributes.remove(oldAttributes[i]);
// if attribute is new, then remove for later storing in correct order
if (oldAttributes[i] != newAttributes[i]) attributes.remove(newAttributes[i]);
attributes.addRegular(newAttributes[i]);
newAttributes[i].setName(oldAttributes[i].getName());
}
return exampleSet;
}
@Override
public ClusterModel generateClusterModel(ExampleSet exampleSet) throws OperatorException {
int k = getParameterAsInt(PARAMETER_K);
int maxOptimizationSteps = getParameterAsInt(PARAMETER_MAX_OPTIMIZATION_STEPS);
boolean useExampleWeights = getParameterAsBoolean(PARAMETER_USE_WEIGHTS);
Kernel kernel = Kernel.createKernel(this);
// init operator progress
getProgress().setTotal(maxOptimizationSteps);
// checking and creating ids if necessary
Tools.checkAndCreateIds(exampleSet);
// additional checks
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this, new String[0]);
if (exampleSet.size() < k) {
throw new UserError(this, 142, k);
}
// extracting attribute names
Attributes attributes = exampleSet.getAttributes();
ArrayList<String> attributeNames = new ArrayList<String>(attributes.size());
for (Attribute attribute : attributes) {
attributeNames.add(attribute.getName());
}
Attribute weightAttribute = attributes.getWeight();
RandomGenerator generator = RandomGenerator.getRandomGenerator(this);
ClusterModel model =
new ClusterModel(
exampleSet,
k,
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_ADD_AS_LABEL),
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_REMOVE_UNLABELED));
// init centroids
int[] clusterAssignments = new int[exampleSet.size()];
for (int i = 0; i < exampleSet.size(); i++) {
clusterAssignments[i] = generator.nextIntInRange(0, k);
}
// run optimization steps
boolean stable = false;
for (int step = 0; step < maxOptimizationSteps && !stable; step++) {
// calculating cluster kernel properties
double[] clusterWeights = new double[k];
double[] clusterKernelCorrection = new double[k];
int i = 0;
for (Example firstExample : exampleSet) {
double firstExampleWeight = useExampleWeights ? firstExample.getValue(weightAttribute) : 1d;
double[] firstExampleValues = getAsDoubleArray(firstExample, attributes);
clusterWeights[clusterAssignments[i]] += firstExampleWeight;
int j = 0;
for (Example secondExample : exampleSet) {
if (clusterAssignments[i] == clusterAssignments[j]) {
double secondExampleWeight =
useExampleWeights ? secondExample.getValue(weightAttribute) : 1d;
clusterKernelCorrection[clusterAssignments[i]] +=
firstExampleWeight
* secondExampleWeight
* kernel.calculateDistance(
firstExampleValues, getAsDoubleArray(secondExample, attributes));
}
j++;
}
i++;
}
for (int z = 0; z < k; z++) {
clusterKernelCorrection[z] /= clusterWeights[z] * clusterWeights[z];
}
// assign examples to new centroids
int[] newClusterAssignments = new int[exampleSet.size()];
i = 0;
for (Example example : exampleSet) {
double[] exampleValues = getAsDoubleArray(example, attributes);
double exampleKernelValue = kernel.calculateDistance(exampleValues, exampleValues);
double nearestDistance = Double.POSITIVE_INFINITY;
int nearestIndex = 0;
for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
double distance = 0;
// iterating over all examples in cluster to get kernel distance
int j = 0;
for (Example clusterExample : exampleSet) {
if (clusterAssignments[j] == clusterIndex) {
distance +=
(useExampleWeights ? clusterExample.getValue(weightAttribute) : 1d)
* kernel.calculateDistance(
getAsDoubleArray(clusterExample, attributes), exampleValues);
}
j++;
}
distance *= -2d / clusterWeights[clusterIndex];
// copy in outer loop
distance += exampleKernelValue;
distance += clusterKernelCorrection[clusterIndex];
if (distance < nearestDistance) {
nearestDistance = distance;
nearestIndex = clusterIndex;
}
}
newClusterAssignments[i] = nearestIndex;
i++;
}
// finishing assignment
stable = true;
for (int j = 0; j < exampleSet.size() && stable; j++) {
stable &= newClusterAssignments[j] == clusterAssignments[j];
}
clusterAssignments = newClusterAssignments;
// trigger operator progress
getProgress().step();
}
// setting last clustering into model
model.setClusterAssignments(clusterAssignments, exampleSet);
getProgress().complete();
if (addsClusterAttribute()) {
Attribute cluster = AttributeFactory.createAttribute("cluster", Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
exampleSet.getAttributes().setCluster(cluster);
int i = 0;
for (Example example : exampleSet) {
example.setValue(cluster, "cluster_" + clusterAssignments[i]);
i++;
}
}
return model;
}
/**
* Parses the provided expression and iterates over the {@link ExampleSet}, interprets attributes
* as variables, evaluates the function and creates a new attribute with the given name that takes
* the expression's value. The type of the attribute depends on the expression type and is {@link
* Ontology#NOMINAL} for strings, {@link Ontology#INTEGER} for integers, {@link Ontology#REAL} for
* reals, {@link Ontology#DATE_TIME} for Dates, and {@link Ontology#BINOMINAL} with values
* "true" and "false" for booleans. If the executing operator is defined,
* there will be a check for stop before the calculation of each example.
*
* @param exampleSet the example set to which the generated attribute is added
* @param name the new attribute name
* @param expression the expression used to generate attribute values
* @param parser the expression parser used to parse the expression argument
* @param resolver the example resolver which is used by the parser to resolve example values
* @param executingOperator the operator calling this method. <code>null</code> is allowed. If not
* null the operator will be used to check for stop
* @throws ProcessStoppedException in case the process was stopped by the user
* @throws ExpressionException in case parsing the expression fails
*/
public static Attribute addAttribute(
ExampleSet exampleSet,
String name,
String expression,
ExpressionParser parser,
ExampleResolver resolver,
Operator executingOperator)
throws ProcessStoppedException, ExpressionException {
// parse the expression
Expression parsedExpression = parser.parse(expression);
Attribute newAttribute = null;
// if != null this needs to be overridden
Attribute existingAttribute = exampleSet.getAttributes().get(name);
StringBuffer appendix = new StringBuffer();
String targetName = name;
if (existingAttribute != null) {
// If an existing attribute will be overridden, first a unique temporary name has to be
// generated by appending a random string to the attribute's name until it's a unique
// attribute name. After the new attribute is build, it's name is set the 'targetName'
// at the end of this method.
//
do {
appendix.append(RandomGenerator.getGlobalRandomGenerator().nextString(5));
} while (exampleSet.getAttributes().get(name + appendix.toString()) != null);
name = name + appendix.toString();
}
ExpressionType resultType = parsedExpression.getExpressionType();
int ontology = resultType.getAttributeType();
if (ontology == Ontology.BINOMINAL) {
newAttribute = AttributeFactory.createAttribute(name, Ontology.BINOMINAL);
newAttribute.getMapping().mapString("false");
newAttribute.getMapping().mapString("true");
} else {
newAttribute = AttributeFactory.createAttribute(name, ontology);
}
// set construction description
newAttribute.setConstruction(expression);
// add new attribute to table and example set
exampleSet.getExampleTable().addAttribute(newAttribute);
exampleSet.getAttributes().addRegular(newAttribute);
// create attribute of correct type and all values
for (Example example : exampleSet) {
if (executingOperator != null) {
executingOperator.checkForStop();
}
// bind example to resolver
resolver.bind(example);
// calculate result
try {
switch (resultType) {
case DOUBLE:
case INTEGER:
example.setValue(newAttribute, parsedExpression.evaluateNumerical());
break;
case DATE:
Date date = parsedExpression.evaluateDate();
example.setValue(newAttribute, date == null ? Double.NaN : date.getTime());
break;
default:
example.setValue(newAttribute, parsedExpression.evaluateNominal());
break;
}
} finally {
// avoid memory leaks
resolver.unbind();
}
}
// remove existing attribute (if necessary)
if (existingAttribute != null) {
AttributeRole oldRole = exampleSet.getAttributes().getRole(existingAttribute);
exampleSet.getAttributes().remove(existingAttribute);
newAttribute.setName(targetName);
// restore role from old attribute to new attribute
if (oldRole.isSpecial()) {
exampleSet.getAttributes().setSpecialAttribute(newAttribute, oldRole.getSpecialName());
}
}
// update example resolver after meta data change
resolver.addAttributeMetaData(
new AttributeMetaData(exampleSet.getAttributes().getRole(newAttribute), exampleSet, true));
return newAttribute;
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// searching confidence attributes
Attributes attributes = exampleSet.getAttributes();
Attribute predictedLabel = attributes.getPredictedLabel();
if (predictedLabel == null) {
throw new UserError(this, 107);
}
NominalMapping mapping = predictedLabel.getMapping();
int numberOfLabels = mapping.size();
Attribute[] confidences = new Attribute[numberOfLabels];
String[] labelValue = new String[numberOfLabels];
int i = 0;
for (String value : mapping.getValues()) {
labelValue[i] = value;
confidences[i] = attributes.getConfidence(value);
if (confidences[i] == null) {
throw new UserError(this, 154, value);
}
i++;
}
// generating new prediction attributes
int k = Math.min(numberOfLabels, getParameterAsInt(PARAMETER_NUMBER_OF_RANKS));
Attribute[] kthPredictions = new Attribute[k];
Attribute[] kthConfidences = new Attribute[k];
for (i = 0; i < k; i++) {
kthPredictions[i] = AttributeFactory.createAttribute(predictedLabel.getValueType());
kthPredictions[i].setName(predictedLabel.getName() + "_" + (i + 1));
kthPredictions[i].setMapping((NominalMapping) predictedLabel.getMapping().clone());
kthConfidences[i] = AttributeFactory.createAttribute(Ontology.REAL);
kthConfidences[i].setName(Attributes.CONFIDENCE_NAME + "_" + (i + 1));
attributes.addRegular(kthPredictions[i]);
attributes.addRegular(kthConfidences[i]);
attributes.setSpecialAttribute(kthPredictions[i], Attributes.PREDICTION_NAME + "_" + (i + 1));
attributes.setSpecialAttribute(kthConfidences[i], Attributes.CONFIDENCE_NAME + "_" + (i + 1));
}
exampleSet.getExampleTable().addAttributes(Arrays.asList(kthConfidences));
exampleSet.getExampleTable().addAttributes(Arrays.asList(kthPredictions));
// now setting values
for (Example example : exampleSet) {
ArrayList<Tupel<Double, Integer>> labelConfidences =
new ArrayList<Tupel<Double, Integer>>(numberOfLabels);
for (i = 0; i < numberOfLabels; i++) {
labelConfidences.add(new Tupel<Double, Integer>(example.getValue(confidences[i]), i));
}
Collections.sort(labelConfidences);
for (i = 0; i < k; i++) {
Tupel<Double, Integer> tupel = labelConfidences.get(numberOfLabels - i - 1);
example.setValue(
kthPredictions[i],
tupel.getSecond()); // Can use index since mapping has been cloned from above
example.setValue(kthConfidences[i], tupel.getFirst());
}
}
// deleting old prediction / confidences
attributes.remove(predictedLabel);
if (getParameterAsBoolean(PARAMETER_REMOVE_OLD_PREDICTIONS)) {
for (i = 0; i < confidences.length; i++) {
attributes.remove(confidences[i]);
}
}
return exampleSet;
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// init
char decimalPointCharacter = getParameterAsString(PARAMETER_DECIMAL_POINT_CHARACTER).charAt(0);
Character groupingCharacter = null;
if (isParameterSet(PARAMETER_NUMBER_GROUPING_CHARACTER)) {
groupingCharacter = getParameterAsString(PARAMETER_NUMBER_GROUPING_CHARACTER).charAt(0);
}
Set<Attribute> attributeSet = attributeSelector.getAttributeSubset(exampleSet, false);
int size = attributeSet.size();
int[] valueTypes = new int[size];
int index = 0;
for (Attribute attribute : attributeSet) {
valueTypes[index++] = attribute.getValueType();
}
// guessing
int[] guessedValueTypes = new int[valueTypes.length];
int checkedCounter = 0;
for (Example example : exampleSet) {
index = 0;
for (Attribute attribute : attributeSet) {
if (!attribute.isNominal() && !attribute.isNumerical()) {
continue;
}
double originalValue = example.getValue(attribute);
if (!Double.isNaN(originalValue)) {
if (guessedValueTypes[index] != Ontology.NOMINAL) {
try {
String valueString = example.getValueAsString(attribute);
if (!Attribute.MISSING_NOMINAL_VALUE.equals(valueString)) {
if (groupingCharacter != null) {
valueString = valueString.replace(groupingCharacter.toString(), "");
}
valueString = valueString.replace(decimalPointCharacter, '.');
double value = Double.parseDouble(valueString);
if (guessedValueTypes[index] != Ontology.REAL) {
if (Tools.isEqual(Math.round(value), value)) {
guessedValueTypes[index] = Ontology.INTEGER;
} else {
guessedValueTypes[index] = Ontology.REAL;
}
}
}
} catch (NumberFormatException e) {
guessedValueTypes[index] = Ontology.NOMINAL;
checkedCounter++;
}
}
}
index++;
}
if (checkedCounter >= guessedValueTypes.length) {
break;
}
}
// the example set contains at least one example and the guessing was performed
if (exampleSet.size() > 0) {
valueTypes = guessedValueTypes;
// new attributes
List<AttributeRole> newAttributes = new LinkedList<AttributeRole>();
index = 0;
for (Attribute attribute : attributeSet) {
if (!attribute.isNominal() && !attribute.isNumerical()) {
continue;
}
AttributeRole role = exampleSet.getAttributes().getRole(attribute);
Attribute newAttribute = AttributeFactory.createAttribute(valueTypes[index]);
exampleSet.getExampleTable().addAttribute(newAttribute);
AttributeRole newRole = new AttributeRole(newAttribute);
newRole.setSpecial(role.getSpecialName());
newAttributes.add(newRole);
// copy data
for (Example e : exampleSet) {
double oldValue = e.getValue(attribute);
if (Ontology.ATTRIBUTE_VALUE_TYPE.isA(valueTypes[index], Ontology.NUMERICAL)) {
if (!Double.isNaN(oldValue)) {
String valueString = e.getValueAsString(attribute);
if (Attribute.MISSING_NOMINAL_VALUE.equals(valueString)) {
e.setValue(newAttribute, Double.NaN);
} else {
if (groupingCharacter != null) {
valueString = valueString.replace(groupingCharacter.toString(), "");
}
valueString = valueString.replace(decimalPointCharacter, '.');
e.setValue(newAttribute, Double.parseDouble(valueString));
}
} else {
e.setValue(newAttribute, Double.NaN);
}
} else {
if (!Double.isNaN(oldValue)) {
String value = e.getValueAsString(attribute);
e.setValue(newAttribute, newAttribute.getMapping().mapString(value));
} else {
e.setValue(newAttribute, Double.NaN);
}
}
}
// delete attribute and rename the new attribute (due to deletion and data scans: no
// more memory used :-)
exampleSet.getExampleTable().removeAttribute(attribute);
exampleSet.getAttributes().remove(role);
newAttribute.setName(attribute.getName());
index++;
}
for (AttributeRole role : newAttributes) {
if (role.isSpecial()) {
exampleSet
.getAttributes()
.setSpecialAttribute(role.getAttribute(), role.getSpecialName());
} else {
exampleSet.getAttributes().addRegular(role.getAttribute());
}
}
}
return exampleSet;
}
@Override
public ExampleSet apply(ExampleSet inputExampleSet) throws OperatorException {
ExampleSet exampleSet = (ExampleSet) inputExampleSet.clone();
Attributes attributes = exampleSet.getAttributes();
if (attributeNames.length != attributes.size()) {
throw new UserError(null, 133, numberOfComponents, attributes.size());
}
// remember attributes that have been removed during training. These will be removed lateron
Attribute[] inputAttributes = new Attribute[getTrainingHeader().getAttributes().size()];
int d = 0;
for (Attribute oldAttribute : getTrainingHeader().getAttributes()) {
inputAttributes[d] = attributes.get(oldAttribute.getName());
d++;
}
// determining number of used components
int numberOfUsedComponents = -1;
if (manualNumber) {
numberOfUsedComponents = numberOfComponents;
} else {
if (proportionThreshold == 0.0d) {
numberOfUsedComponents = -1;
} else {
numberOfUsedComponents = 0;
while (cumulativeSingularValueProportion[numberOfUsedComponents] < proportionThreshold) {
numberOfUsedComponents++;
}
numberOfUsedComponents++;
}
}
// if nothing defined or number exceeds maximal number of possible components
if (numberOfUsedComponents == -1 || numberOfUsedComponents > getNumberOfComponents()) {
// keep all components
numberOfUsedComponents = getNumberOfComponents();
}
// retrieve factors inside singularValueVectors
double[][] singularValueFactors = new double[numberOfUsedComponents][attributeNames.length];
double[][] vMatrixData = vMatrix.getArray();
for (int i = 0; i < numberOfUsedComponents; i++) {
double invertedSingularValue = 1d / singularValues[i];
for (int j = 0; j < attributeNames.length; j++) {
singularValueFactors[i][j] = vMatrixData[j][i] * invertedSingularValue;
}
}
// now build new attributes
Attribute[] derivedAttributes = new Attribute[numberOfUsedComponents];
for (int i = 0; i < numberOfUsedComponents; i++) {
if (useLegacyNames) {
derivedAttributes[i] = AttributeFactory.createAttribute("d" + i, Ontology.REAL);
} else {
derivedAttributes[i] = AttributeFactory.createAttribute("svd_" + (i + 1), Ontology.REAL);
}
exampleSet.getExampleTable().addAttribute(derivedAttributes[i]);
attributes.addRegular(derivedAttributes[i]);
}
// now iterator through all examples and derive value of new features
double[] derivedValues = new double[numberOfUsedComponents];
for (Example example : exampleSet) {
// calculate values of new attributes with single scan over attributes
d = 0;
for (Attribute attribute : inputAttributes) {
double attributeValue = example.getValue(attribute);
for (int i = 0; i < numberOfUsedComponents; i++) {
derivedValues[i] += singularValueFactors[i][d] * attributeValue;
}
d++;
}
// set values
for (int i = 0; i < numberOfUsedComponents; i++) {
example.setValue(derivedAttributes[i], derivedValues[i]);
}
// set values back
Arrays.fill(derivedValues, 0);
}
// now remove attributes if needed
if (!keepAttributes) {
for (Attribute attribute : inputAttributes) {
attributes.remove(attribute);
}
}
return exampleSet;
}
public ExampleTable getExampleTable() {
return parent.getExampleTable();
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
exampleSet.recalculateAllAttributeStatistics();
Attributes attributes = exampleSet.getAttributes();
if (attributeNames.length != attributes.size()) {
throw new UserError(null, 133, numberOfComponents, attributes.size());
}
// remember attributes that have been removed during training. These will be removed lateron
Attribute[] inputAttributes = new Attribute[getTrainingHeader().getAttributes().size()];
int d = 0;
for (Attribute oldAttribute : getTrainingHeader().getAttributes()) {
inputAttributes[d] = attributes.get(oldAttribute.getName());
d++;
}
// determining number of used components
int numberOfUsedComponents = -1;
if (manualNumber) {
numberOfUsedComponents = numberOfComponents;
} else {
if (varianceThreshold == 0.0d) {
numberOfUsedComponents = -1;
} else {
numberOfUsedComponents = 0;
while (cumulativeVariance[numberOfUsedComponents] < varianceThreshold) {
numberOfUsedComponents++;
}
numberOfUsedComponents++;
if (numberOfUsedComponents == eigenVectors.size()) {
numberOfUsedComponents--;
}
}
}
if (numberOfUsedComponents == -1) {
// keep all components
numberOfUsedComponents = attributes.size();
}
// retrieve factors inside eigenVectors
double[][] eigenValueFactors = new double[numberOfUsedComponents][attributeNames.length];
for (int i = 0; i < numberOfUsedComponents; i++) {
eigenValueFactors[i] = this.eigenVectors.get(i).getEigenvector();
}
// now build new attributes
Attribute[] derivedAttributes = new Attribute[numberOfUsedComponents];
for (int i = 0; i < numberOfUsedComponents; i++) {
derivedAttributes[i] = AttributeFactory.createAttribute("pc_" + (i + 1), Ontology.REAL);
exampleSet.getExampleTable().addAttribute(derivedAttributes[i]);
attributes.addRegular(derivedAttributes[i]);
}
// now iterator through all examples and derive value of new features
double[] derivedValues = new double[numberOfUsedComponents];
for (Example example : exampleSet) {
// calculate values of new attributes with single scan over attributes
d = 0;
for (Attribute attribute : inputAttributes) {
double attributeValue = example.getValue(attribute) - means[d];
for (int i = 0; i < numberOfUsedComponents; i++) {
derivedValues[i] += eigenValueFactors[i][d] * attributeValue;
}
d++;
}
// set values
for (int i = 0; i < numberOfUsedComponents; i++) {
example.setValue(derivedAttributes[i], derivedValues[i]);
}
// set values back
Arrays.fill(derivedValues, 0);
}
// now remove attributes if needed
if (!keepAttributes) {
for (Attribute attribute : inputAttributes) {
attributes.remove(attribute);
}
}
return exampleSet;
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
String labelName = getParameterAsString(PARAMETER_LABEL_NAME_STEM);
int horizon = getParameterAsInt(PARAMETER_HORIZON);
// TODO: check if appropriate label is there
// TODO: check if window width is large enough
// collect base names and attributes to remove, find label
Attribute labelAttribute = null;
List<String> baseNames = new LinkedList<String>();
List<Attribute> toRemove = new LinkedList<Attribute>();
int windowWidth = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
if (attribute.getName().endsWith("-0")) {
String baseName = attribute.getName().substring(0, attribute.getName().lastIndexOf("-"));
baseNames.add(baseName);
if (attribute.getName().startsWith(labelName)) {
labelAttribute = attribute;
}
}
int index =
Integer.valueOf(attribute.getName().substring(attribute.getName().lastIndexOf("-") + 1));
windowWidth = Math.max(windowWidth, index);
if ((index >= 0) && (index < horizon)) {
toRemove.add(attribute);
}
}
// remove horizon attributes
for (Attribute attribute : toRemove) {
exampleSet.getAttributes().remove(attribute);
}
// set label
exampleSet.getAttributes().setLabel(labelAttribute);
// transform all values relative to last known label attribute value and create base value
// column
if (getParameterAsBoolean(PARAMETER_RELATIVE_TRANSFORMATION)) {
if (labelAttribute.isNumerical()) {
Attribute baseValueAttribute =
AttributeFactory.createAttribute("base_value", Ontology.REAL);
exampleSet.getExampleTable().addAttribute(baseValueAttribute);
exampleSet.getAttributes().setSpecialAttribute(baseValueAttribute, "base_value");
for (Example example : exampleSet) {
// handle label
String lastKnownLabelName = labelName + "-" + horizon;
Attribute lastKnownLabelAttribute = exampleSet.getAttributes().get(lastKnownLabelName);
double baseLabelValue = example.getValue(lastKnownLabelAttribute);
example.setValue(baseValueAttribute, baseLabelValue);
for (String baseName : baseNames) {
String lastKnownBaseName = baseName + "-" + horizon;
Attribute lastKnownBaseAttribute = exampleSet.getAttributes().get(lastKnownBaseName);
double baseAttributeValue = example.getValue(lastKnownBaseAttribute);
for (int w = horizon; w <= windowWidth; w++) {
String currentName = baseName + "-" + w;
Attribute currentAttribute = exampleSet.getAttributes().get(currentName);
double currentValue = example.getValue(currentAttribute);
example.setValue(currentAttribute, currentValue - baseAttributeValue);
}
}
example.setValue(labelAttribute, example.getValue(labelAttribute) - baseLabelValue);
}
}
}
return new IOObject[] {exampleSet};
}
