T
- public abstract class Metric<T extends InformationLoss<?>>
extends java.lang.Object
implements java.io.Serializable
Modifier and Type | Class and Description |
---|---|
static class |
Metric.AggregateFunction
Pluggable aggregate functions.
|
Modifier and Type | Method and Description |
---|---|
static Metric<ILSingleDimensional> |
createAECSMetric()
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
createAECSMetric(double gsFactor)
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
createAmbiguityMetric()
Creates an instance of the ambiguity metric.
|
static Metric<ILSingleDimensional> |
createClassificationMetric()
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
createClassificationMetric(double gsFactor)
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
createDiscernabilityMetric()
Creates an instance of the discernability metric.
|
static Metric<ILSingleDimensional> |
createDiscernabilityMetric(boolean monotonic)
Creates an instance of the discernability metric.
|
static MetricSDNMEntropyBasedInformationLoss |
createEntropyBasedInformationLossMetric()
Creates an instance of the entropy-based information loss metric, which will treat
generalization and suppression equally.
|
static MetricSDNMEntropyBasedInformationLoss |
createEntropyBasedInformationLossMetric(double gsFactor)
Creates an instance of the entropy-based information loss metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric()
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric(boolean monotonic)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric(boolean monotonic,
double gsFactor)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createEntropyMetric(double gsFactor)
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createHeightMetric()
Creates an instance of the height metric.
|
static Metric<AbstractILMultiDimensional> |
createHeightMetric(Metric.AggregateFunction function)
Creates an instance of the height metric.
|
InformationLoss<?> |
createInstanceOfHighestScore()
Returns an instance of the highest possible score.
|
InformationLoss<?> |
createInstanceOfLowestScore()
Returns an instance of the lowest possible score.
|
static Metric<ILSingleDimensional> |
createKLDivergenceMetric()
Creates an instance of the KL Divergence metric.
|
static Metric<AbstractILMultiDimensional> |
createLossMetric()
Creates an instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
createLossMetric(double gsFactor)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
createLossMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
createLossMetric(Metric.AggregateFunction function)
Creates an instance of the loss metric which treats generalization and suppression equally.
|
abstract InformationLoss<?> |
createMaxInformationLoss()
Deprecated.
|
static Metric<?> |
createMetric(Metric<?> metric,
int minLevel,
int maxLevel)
This method supports backwards compatibility.
|
abstract InformationLoss<?> |
createMinInformationLoss()
Deprecated.
|
static Metric<AbstractILMultiDimensional> |
createNormalizedEntropyMetric()
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createNormalizedEntropyMetric(Metric.AggregateFunction function)
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric()
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(boolean monotonic)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(boolean monotonic,
double gsFactor)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(double gsFactor)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecisionMetric(Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold,
boolean monotonic)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedEntropyMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedLossMetric(double threshold)
Creates a potentially precomputed instance of the loss metric which treats generalization
and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedLossMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedLossMetric(double threshold,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedLossMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedNormalizedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
createPrecomputedNormalizedEntropyMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static MetricSDNMPublisherPayout |
createPublisherPayoutMetric(boolean journalistAttackerModel)
Creates an instance of the model for maximizing publisher benefit in the game-theoretic privacy
model based on a cost/benefit analysis.
|
static MetricSDNMPublisherPayout |
createPublisherPayoutMetric(boolean journalistAttackerModel,
double gsFactor)
Creates an instance of the model for maximizing publisher benefit in the game-theoretic privacy
model based on a cost/benefit analysis.
|
static Metric<AbstractILMultiDimensional> |
createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)
Creates an instance of a metric with statically defined information loss.
|
static Metric<AbstractILMultiDimensional> |
createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss,
Metric.AggregateFunction function)
Creates an instance of a metric with statically defined information loss.
|
Metric.AggregateFunction |
getAggregateFunction()
Returns the aggregate function of a multi-dimensional metric, null otherwise.
|
MetricConfiguration |
getConfiguration()
Returns the configuration of this metric.
|
MetricDescription |
getDescription()
Returns a description of this metric.
|
double |
getGeneralizationFactor()
Returns the factor used weight generalized values.
|
double |
getGeneralizationSuppressionFactor()
Returns the factor weighting generalization and suppression.
|
InformationLossWithBound<T> |
getInformationLoss(Transformation<?> node,
HashGroupify groupify)
Evaluates the metric for the given node.
|
InformationLossWithBound<T> |
getInformationLoss(Transformation<?> node,
HashGroupifyEntry entry)
Returns the information loss that would be induced by suppressing the given entry.
|
T |
getLowerBound(Transformation<?> node)
Returns a lower bound for the information loss for the given node.
|
T |
getLowerBound(Transformation<?> node,
HashGroupify groupify)
Returns a lower bound for the information loss for the given node.
|
java.lang.String |
getName()
Returns the name of metric.
|
ILScore |
getScore(Transformation<?> node,
HashGroupify groupify)
Calculates the score.
|
double |
getSuppressionFactor()
Returns the factor used to weight suppressed values.
|
void |
initialize(DataManager manager,
DataDefinition definition,
Data input,
GeneralizationHierarchy[] hierarchies,
ARXConfiguration config)
Initializes the metric.
|
boolean |
isAbleToHandleClusteredMicroaggregation()
Returns whether this metric handles clustering and microaggregation
|
boolean |
isAbleToHandleMicroaggregation()
Returns whether this metric handles microaggregation
|
boolean |
isGSFactorSupported()
Returns whether a generalization/suppression factor is supported
|
boolean |
isIndependent()
Returns whether this metric requires the transformed data or groups to
determine information loss.
|
boolean |
isMonotonic(double suppressionLimit)
Returns whether this model is monotonic under the given suppression limit.
|
boolean |
isMonotonicWithGeneralization()
Returns false if the metric is non-monotonic when using generalization.
|
boolean |
isMonotonicWithSuppression()
Returns false if the metric is non-monotonic when using suppression.
|
boolean |
isMultiDimensional()
Returns true if the metric is multi-dimensional.
|
boolean |
isPrecomputed()
Returns whether the metric is precomputed
|
boolean |
isScoreFunctionSupported()
Returns whether the metric provides a score function
|
boolean |
isWeighted()
Returns true if the metric is weighted.
|
static java.util.List<MetricDescription> |
list()
Returns a list of all available metrics for information loss.
|
abstract ElementData |
render(ARXConfiguration config)
Renders the privacy model
|
java.lang.String |
toString()
Returns the name of metric.
|
public static Metric<ILSingleDimensional> createAECSMetric()
public static Metric<ILSingleDimensional> createAECSMetric(double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<ILSingleDimensional> createAmbiguityMetric()
public static Metric<ILSingleDimensional> createClassificationMetric()
public static Metric<ILSingleDimensional> createClassificationMetric(double gsFactor)
gsFactor
- public static Metric<ILSingleDimensional> createDiscernabilityMetric()
public static Metric<ILSingleDimensional> createDiscernabilityMetric(boolean monotonic)
monotonic
- If set to true, the monotonic variant (DM*) will be createdpublic static MetricSDNMEntropyBasedInformationLoss createEntropyBasedInformationLossMetric()
public static MetricSDNMEntropyBasedInformationLoss createEntropyBasedInformationLossMetric(double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createEntropyMetric()
public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic)
monotonic
- If set to true, the monotonic variant of the metric will be createdpublic static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, Metric.AggregateFunction function)
monotonic
- If set to true, the monotonic variant of the metric will be createdfunction
- The aggregate function to be used for comparing resultspublic static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.monotonic
- If set to true, the monotonic variant of the metric will be createdpublic static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)
monotonic
- If set to true, the monotonic variant of the metric will be createdgsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.function
- The aggregate function to be used for comparing resultspublic static Metric<AbstractILMultiDimensional> createEntropyMetric(double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createHeightMetric()
public static Metric<AbstractILMultiDimensional> createHeightMetric(Metric.AggregateFunction function)
function
- The aggregate function to use for comparing resultspublic static Metric<ILSingleDimensional> createKLDivergenceMetric()
public static Metric<AbstractILMultiDimensional> createLossMetric()
public static Metric<AbstractILMultiDimensional> createLossMetric(Metric.AggregateFunction function)
function
- The aggregate function to use for comparing resultspublic static Metric<AbstractILMultiDimensional> createLossMetric(double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createLossMetric(double gsFactor, Metric.AggregateFunction function)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.function
- The aggregate function to use for comparing resultspublic static Metric<?> createMetric(Metric<?> metric, int minLevel, int maxLevel)
metric
- minLevel
- maxLevel
- public static Metric<AbstractILMultiDimensional> createNormalizedEntropyMetric()
public static Metric<AbstractILMultiDimensional> createNormalizedEntropyMetric(Metric.AggregateFunction function)
function
- The aggregate function to use for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecisionMetric()
public static Metric<AbstractILMultiDimensional> createPrecisionMetric(Metric.AggregateFunction function)
function
- The aggregate function to use for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic)
monotonic
- If set to true, the monotonic variant of the metric will be createdpublic static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, Metric.AggregateFunction function)
monotonic
- If set to true, the monotonic variant of the metric will be createdfunction
- public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, double gsFactor)
monotonic
- If set to true, the monotonic variant of the metric will be createdgsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)
monotonic
- If set to true, the monotonic variant of the metric will be createdgsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.function
- public static Metric<AbstractILMultiDimensional> createPrecisionMetric(double gsFactor)
gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createPrecisionMetric(double gsFactor, Metric.AggregateFunction function)
function
- The aggregate function to use for comparing resultsgsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.monotonic
- If set to true, the monotonic variant of the metric will be createdpublic static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, Metric.AggregateFunction function)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.monotonic
- If set to true, the monotonic variant of the metric will be createdfunction
- The aggregate function to be used for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.monotonic
- If set to true, the monotonic variant of the metric will be createdpublic static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor, Metric.AggregateFunction function)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.monotonic
- If set to true, the monotonic variant of the metric will be createdgsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.function
- The aggregate function to be used for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, double gsFactor)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold, Metric.AggregateFunction function)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.function
- The aggregate function to use for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold, double gsFactor)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold, double gsFactor, Metric.AggregateFunction function)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.gsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.function
- The aggregate function to use for comparing resultspublic static Metric<AbstractILMultiDimensional> createPrecomputedNormalizedEntropyMetric(double threshold)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.public static Metric<AbstractILMultiDimensional> createPrecomputedNormalizedEntropyMetric(double threshold, Metric.AggregateFunction function)
threshold
- The precomputed variant of the metric will be used if
#distinctValues / #rows <= threshold for all quasi-identifiers.function
- The aggregate function to use for comparing resultspublic static MetricSDNMPublisherPayout createPublisherPayoutMetric(boolean journalistAttackerModel)
journalistAttackerModel
- If set to true, the journalist attacker model will be assumed,
the prosecutor model will be assumed, otherwisepublic static MetricSDNMPublisherPayout createPublisherPayoutMetric(boolean journalistAttackerModel, double gsFactor)
journalistAttackerModel
- If set to true, the journalist attacker model will be assumed,
the prosecutor model will be assumed, otherwisegsFactor
- A factor [0,1] weighting generalization and suppression.
The default value is 0.5, which means that generalization
and suppression will be treated equally. A factor of 0
will favor suppression, and a factor of 1 will favor
generalization. The values in between can be used for
balancing both methods.public static Metric<AbstractILMultiDimensional> createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)
loss
- User defined information loss per attributepublic static Metric<AbstractILMultiDimensional> createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss, Metric.AggregateFunction function)
loss
- User defined information loss per attributefunction
- The aggregate function to use for comparing resultspublic static java.util.List<MetricDescription> list()
public InformationLoss<?> createInstanceOfHighestScore()
public InformationLoss<?> createInstanceOfLowestScore()
@Deprecated public abstract InformationLoss<?> createMaxInformationLoss()
@Deprecated public abstract InformationLoss<?> createMinInformationLoss()
public Metric.AggregateFunction getAggregateFunction()
public MetricConfiguration getConfiguration()
public MetricDescription getDescription()
public double getGeneralizationFactor()
public double getGeneralizationSuppressionFactor()
public final InformationLossWithBound<T> getInformationLoss(Transformation<?> node, HashGroupify groupify)
node
- The node for which to compute the information lossgroupify
- The groupify operator of the previous checkpublic final InformationLossWithBound<T> getInformationLoss(Transformation<?> node, HashGroupifyEntry entry)
getInformationLoss(node, groupify)
but is guaranteed to be comparable for
different entries from the same groupify operator.entry
- public T getLowerBound(Transformation<?> node)
null
.node
- public T getLowerBound(Transformation<?> node, HashGroupify groupify)
null
.node
- groupify
- public java.lang.String getName()
public ILScore getScore(Transformation<?> node, HashGroupify groupify)
node
- groupify
- public double getSuppressionFactor()
public final void initialize(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)
manager
- definition
- input
- hierarchies
- config
- public boolean isAbleToHandleMicroaggregation()
public boolean isAbleToHandleClusteredMicroaggregation()
public boolean isGSFactorSupported()
public boolean isIndependent()
public final boolean isMonotonic(double suppressionLimit)
suppressionLimit
- public final boolean isMonotonicWithGeneralization()
public final boolean isMonotonicWithSuppression()
public final boolean isMultiDimensional()
public boolean isPrecomputed()
public boolean isScoreFunctionSupported()
public final boolean isWeighted()
public abstract ElementData render(ARXConfiguration config)
public java.lang.String toString()
toString
in class java.lang.Object