de.lmu.ifi.dbs.elki.algorithm.statistics

Class EvaluateRankingQuality<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<V,CollectionResult<DoubleVector>>

de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRankingQuality<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<V>

@Title(value="Evaluate Ranking Quality")
@Description(value="Evaluates the effectiveness of a distance function via the obtained rankings.")
public class EvaluateRankingQuality<V extends NumberVector>
extends AbstractDistanceBasedAlgorithm<V,CollectionResult<DoubleVector>>

Evaluate a distance function with respect to kNN queries. For each point, the neighbors are sorted by distance, then the ROC AUC is computed. A score of 1 means that the distance function provides a perfect ordering of relevant neighbors first, then irrelevant neighbors. A value of 0.5 can be obtained by random sorting. A value of 0 means the distance function is inverted, i.e. a similarity. In contrast to RankingQualityHistogram, this method uses a binning based on the centrality of objects. This allows analyzing whether or not a particular distance degrades for the outer parts of a cluster. TODO: Allow fixed binning range, configurable TODO: Add sampling

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EvaluateRankingQuality.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.
(package private) int	numbins Number of bins to use.

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction, int numbins) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
HistogramResult<DoubleVector>	run(Database database) Runs the algorithm.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

LOG

private static final Logging LOG

The logger for this class.

numbins

int numbins

Number of bins to use.

Constructor Detail

EvaluateRankingQuality

public EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction,
                      int numbins)

Constructor.

Parameters:

distanceFunction - Distance function

numbins - Number of bins

Method Detail

run

public HistogramResult<DoubleVector> run(Database database)

Description copied from interface: Algorithm

Runs the algorithm.

Specified by:

run in interface Algorithm

Overrides:

run in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Returns:

the static logger