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

Class EvaluateRetrievalPerformance<O>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,EvaluateRetrievalPerformance.RetrievalPerformanceResult>

de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRetrievalPerformance<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O>

public class EvaluateRetrievalPerformance<O>
extends AbstractDistanceBasedAlgorithm<O,EvaluateRetrievalPerformance.RetrievalPerformanceResult>

Evaluate a distance functions performance by computing the mean average precision, ROC, and NN classification performance when ranking the objects by distance.

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EvaluateRetrievalPerformance.KNNEvaluator Evaluate kNN retrieval performance.
static class	EvaluateRetrievalPerformance.Parameterizer<O> Parameterization class.
static class	EvaluateRetrievalPerformance.RetrievalPerformanceResult Result object for MAP scores.

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	includeSelf Include query object in evaluation.
private static Logging	LOG The logger for this class.
protected int	maxk K nearest neighbors to use for classification evaluation.
private String	PREFIX Prefix for statistics.
protected RandomFactory	random Random sampling seed.
protected double	sampling Relative number of object to use in sampling.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
EvaluateRetrievalPerformance(DistanceFunction<? super O> distanceFunction, double sampling, RandomFactory random, boolean includeSelf, int maxk) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private void	computeDistances(ModifiableDoubleDBIDList nlist, DBIDIter query, DistanceQuery<O> distQuery, Relation<O> relation) Compute the distances to the neighbor objects.
private void	findMatches(ModifiableDBIDs posn, Relation<?> lrelation, Object label) Find all matching objects.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
protected static boolean	match(Object ref, Object test) Test whether two relation agree.
EvaluateRetrievalPerformance.RetrievalPerformanceResult	run(Database database, Relation<O> relation, Relation<?> lrelation) Run 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, run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

sampling

protected double sampling

Relative number of object to use in sampling.

random

protected RandomFactory random

Random sampling seed.

includeSelf

protected boolean includeSelf

Include query object in evaluation.

PREFIX

private final String PREFIX

Prefix for statistics.

maxk

protected int maxk

K nearest neighbors to use for classification evaluation.

Constructor Detail

EvaluateRetrievalPerformance

public EvaluateRetrievalPerformance(DistanceFunction<? super O> distanceFunction,
                            double sampling,
                            RandomFactory random,
                            boolean includeSelf,
                            int maxk)

Constructor.

Parameters:

distanceFunction - Distance function

sampling - Sampling rate

random - Random sampling generator

includeSelf - Include query object in evaluation

maxk - Maximum k for kNN evaluation

Method Detail

run

public EvaluateRetrievalPerformance.RetrievalPerformanceResult run(Database database,
                                                          Relation<O> relation,
                                                          Relation<?> lrelation)

Run the algorithm

Parameters:

database - Database to run on (for kNN queries)

relation - Relation for distance computations

lrelation - Relation for class label comparison

Returns:

Vectors containing mean and standard deviation.

match

protected static boolean match(Object ref,
            Object test)

Test whether two relation agree.

Parameters:

ref - Reference object

test - Test object

Returns:

true if the objects match

findMatches

private void findMatches(ModifiableDBIDs posn,
               Relation<?> lrelation,
               Object label)

Find all matching objects.

Parameters:

posn - Output set.

lrelation - Label relation

label - Query object label

computeDistances

private void computeDistances(ModifiableDoubleDBIDList nlist,
                    DBIDIter query,
                    DistanceQuery<O> distQuery,
                    Relation<O> relation)

Compute the distances to the neighbor objects.

Parameters:

nlist - Neighbor list (output)

query - Query object

distQuery - Distance function

relation - Data relation

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<EvaluateRetrievalPerformance.RetrievalPerformanceResult>

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<EvaluateRetrievalPerformance.RetrievalPerformanceResult>

Returns:

the static logger