Package org.deidentifier.arx.metric.v2

Klasse __MetricV2

java.lang.Object
org.deidentifier.arx.metric.v2.__MetricV2
public class __MetricV2 extends Object
This internal class provides access to version 2 of all metrics. Users of the API should use org.deidentifier.arx.metric.Metric for creating instances of metrics for information loss.

  • Konstruktordetails

    • __MetricV2

      public __MetricV2()

  • Methodendetails

    • createAECSMetric

      public static Metric<ILSingleDimensional> createAECSMetric()
      Creates a new instance of the AECS metric.
      Gibt zurück:

    • createAECSMetric

      public static Metric<ILSingleDimensional> createAECSMetric(double gsFactor)
      Creates a new instance of the AECS metric.
      Parameter:
      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.
      Gibt zurück:

    • createAECSMetric

      public static Metric<ILSingleDimensional> createAECSMetric(int rowCount)
      Creates a new instance of the AECS metric.
      Parameter:
      rowCount -
      Gibt zurück:

    • createAmbiguityMetric

      public static Metric<ILSingleDimensional> createAmbiguityMetric()
      Creates an instance of the ambiguity metric.
      Gibt zurück:

    • createClassificationMetric

      public static Metric<ILSingleDimensional> createClassificationMetric(double gsFactor)
      Creates an instance of the classification metric.
      Parameter:
      gsFactor -
      Gibt zurück:

    • createClassificationMetric

      public static Metric<ILSingleDimensional> createClassificationMetric()
      Creates an instance of the classification metric.
      Gibt zurück:

    • createDiscernabilityMetric

      public static Metric<ILSingleDimensional> createDiscernabilityMetric()
      Creates an instance of the discernability metric.
      Gibt zurück:

    • createDiscernabilityMetric

      public static Metric<ILSingleDimensional> createDiscernabilityMetric(boolean monotonic)
      Creates an instance of the discernability metric. The monotonic variant is DM*.
      Parameter:
      monotonic - If set to true, the monotonic variant (DM*) will be created
      Gibt zurück:

    • createDiscernabilityMetric

      public static Metric<ILSingleDimensional> createDiscernabilityMetric(boolean monotonic, double numTuples)
      Creates an instance of the discernability metric. The monotonic variant is DM*.
      Parameter:
      monotonic - If set to true, the monotonic variant (DM*) will be created
      numTuples - Pre-initialization
      Gibt zurück:

    • createEntropyBasedInformationLossMetric

      public static MetricSDNMEntropyBasedInformationLoss createEntropyBasedInformationLossMetric(double gsFactor)
      Creates an instance of the entropy-based information loss metric.
      Parameter:
      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.
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric()
      Creates an instance of the non-monotonic non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic)
      Creates an instance of the non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, Metric.AggregateFunction function)
      Creates an instance of the non-uniform entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      function - The aggregate function to be used for comparing results
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, double gsFactor)
      Creates an instance of the non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      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.
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)
      Creates an instance of the non-uniform entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      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 be used for comparing results
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(boolean monotonic, double[][] cache, int[][][] cardinalities, int[][][] hierarchies)
      Creates an instance of the non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      cache -
      cardinalities -
      hierarchies -
      Gibt zurück:

    • createEntropyMetric

      public static Metric<AbstractILMultiDimensional> createEntropyMetric(double gsFactor)
      Creates an instance of the non-monotonic non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      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.
      Gibt zurück:

    • createHeightMetric

      public static Metric<AbstractILMultiDimensional> createHeightMetric()
      Creates an instance of the height metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Gibt zurück:

    • createHeightMetric

      public static Metric<AbstractILMultiDimensional> createHeightMetric(Metric.AggregateFunction function)
      Creates an instance of the height metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createHeightMetric

      public static Metric<AbstractILMultiDimensional> createHeightMetric(int minHeight, int maxHeight)
      Creates an instance of the height metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      minHeight -
      maxHeight -
      Gibt zurück:

    • createILMultiDimensionalArithmeticMean

      public static InformationLoss<?> createILMultiDimensionalArithmeticMean(double value)
      Helper method. Normally, there should be no need to call this
      Parameter:
      value -
      Gibt zurück:

    • createILMultiDimensionalSum

      public static InformationLoss<?> createILMultiDimensionalSum(double value)
      Helper method. Normally, there should be no need to call this
      Parameter:
      value -
      Gibt zurück:

    • createILSingleDimensional

      public static InformationLoss<?> createILSingleDimensional(double value)
      Helper method. Normally, there should be no need to call this
      Parameter:
      value -
      Gibt zurück:

    • createKLDivergenceMetric

      public static Metric<ILSingleDimensional> createKLDivergenceMetric()
      Creates an instance of the KL Divergence metric.
      Gibt zurück:

    • createLossMetric

      public static Metric<AbstractILMultiDimensional> createLossMetric()
      Creates an instance of the loss metric which treats generalization and suppression equally. The default aggregate function, which is the geometric mean, will be used. This metric will respect attribute weights defined in the configuration.
      Gibt zurück:

    • createLossMetric

      public static Metric<AbstractILMultiDimensional> createLossMetric(Metric.AggregateFunction function)
      Creates an instance of the loss metric which treats generalization and suppression equally. This metric will respect attribute weights defined in the configuration.
      Parameter:
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createLossMetric

      public static Metric<AbstractILMultiDimensional> createLossMetric(double gsFactor)
      Creates an instance of the loss metric with factors for weighting generalization and suppression. The default aggregate function, which is the geometric mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      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.
      Gibt zurück:

    • createLossMetric

      public static Metric<AbstractILMultiDimensional> createLossMetric(double gsFactor, Metric.AggregateFunction function)
      Creates an instance of the loss metric with factors for weighting generalization and suppression. This metric will respect attribute weights defined in the configuration.
      Parameter:
      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 results
      Gibt zurück:

    • createNormalizedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createNormalizedEntropyMetric()
      Creates an instance of the normalized entropy metric. The default aggregate function, which is the sum function, will be used. This metric will respect attribute weights defined in the configuration.
      Gibt zurück:

    • createNormalizedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createNormalizedEntropyMetric(Metric.AggregateFunction function)
      Creates an instance of the normalized entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric()
      Creates an instance of the non-monotonic precision metric. The default aggregate function, which is the arithmetic mean, will be used. This metric will respect attribute weights defined in the configuration.
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(Metric.AggregateFunction function)
      Creates an instance of the non-monotonic precision metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic)
      Creates an instance of the precision metric. The default aggregate function, which is the arithmetic mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, Metric.AggregateFunction function)
      Creates an instance of the precision metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      function -
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, double gsFactor)
      Creates an instance of the precision metric. The default aggregate function, which is the arithmetic mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      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.
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)
      Creates an instance of the precision metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      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 -
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(boolean monotonic, int[] heights, double cells)
      Creates an instance of the precision metric. The default aggregate function, which is the arithmetic mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      monotonic - If set to true, the monotonic variant of the metric will be created
      heights -
      cells -
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(double gsFactor)
      Creates an instance of the non-monotonic precision metric. The default aggregate function, which is the arithmetic mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      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.
      Gibt zurück:

    • createPrecisionMetric

      public static Metric<AbstractILMultiDimensional> createPrecisionMetric(double gsFactor, Metric.AggregateFunction function)
      Creates an instance of the non-monotonic precision metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      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 results
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold)
      Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic)
      Creates a potentially precomputed instance of the non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      monotonic - If set to true, the monotonic variant of the metric will be created
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, Metric.AggregateFunction function)
      Creates a potentially precomputed instance of the non-uniform entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      monotonic - If set to true, the monotonic variant of the metric will be created
      function - The aggregate function to be used for comparing results
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor)
      Creates a potentially precomputed instance of the non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      monotonic - If set to true, the monotonic variant of the metric will be created
      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.
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor, Metric.AggregateFunction function)
      Creates a potentially precomputed instance of the non-uniform entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      monotonic - If set to true, the monotonic variant of the metric will be created
      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 be used for comparing results
      Gibt zurück:

    • createPrecomputedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedEntropyMetric(double threshold, double gsFactor)
      Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= 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.
      Gibt zurück:

    • createPrecomputedLossMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold)
      Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally. The default aggregate function, which is the geometric mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      Gibt zurück:

    • createPrecomputedLossMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold, Metric.AggregateFunction function)
      Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createPrecomputedLossMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedLossMetric(double threshold, double gsFactor)
      Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression. The default aggregate function, which is the gemetric mean, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= 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.
      Gibt zurück:

    • createPrecomputedLossMetric

      public 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. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= 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 results
      Gibt zurück:

    • createPrecomputedNormalizedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedNormalizedEntropyMetric(double threshold)
      Creates a potentially precomputed instance of the normalized entropy metric. The default aggregate function, which is the sum function, will be used. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      Gibt zurück:

    • createPrecomputedNormalizedEntropyMetric

      public static Metric<AbstractILMultiDimensional> createPrecomputedNormalizedEntropyMetric(double threshold, Metric.AggregateFunction function)
      Creates a potentially precomputed instance of the normalized entropy metric. This metric will respect attribute weights defined in the configuration.
      Parameter:
      threshold - The precomputed variant of the metric will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.
      function - The aggregate function to use for comparing results
      Gibt zurück:

    • createPublisherBenefitMetric

      public static MetricSDNMPublisherPayout createPublisherBenefitMetric(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.
      Parameter:
      journalistAttackerModel - If set to true, the journalist attacker model will be assumed, the prosecutor model will be assumed, otherwise
      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.
      Gibt zurück:

    • createStaticMetric

      public static Metric<AbstractILMultiDimensional> createStaticMetric(Map<String,List<Double>> loss)
      Creates an instance of a metric with statically defined information loss. The default aggregate function, which is the sum-function, will be used for comparing results. This metric will respect attribute weights defined in the configuration.
      Parameter:
      loss - User defined information loss per attribute
      Gibt zurück:

    • createStaticMetric

      public static Metric<AbstractILMultiDimensional> createStaticMetric(Map<String,List<Double>> loss, Metric.AggregateFunction function)
      Creates an instance of a metric with statically defined information loss. This metric will respect attribute weights defined in the configuration.
      Parameter:
      loss - User defined information loss per attribute
      function - The aggregate function to use for comparing results
      Gibt zurück: