de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Class SigmoidOutlierScaling

java.lang.Object

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SigmoidOutlierScaling

All Implemented Interfaces:

OutlierScaling, ScalingFunction

@Reference(authors="J. Gao, P.-N. Tan",
           title="Converting Output Scores from Outlier Detection Algorithms into Probability Estimates",
           booktitle="Proc. Sixth International Conference on Data Mining, 2006. ICDM\'06.",
           url="https://doi.org/10.1109/ICDM.2006.43",
           bibkey="DBLP:conf/icdm/GaoT06")
 @Alias(value="de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SigmoidOutlierScalingFunction")
public class SigmoidOutlierScaling
extends java.lang.Object
implements OutlierScaling

Tries to fit a sigmoid to the outlier scores and use it to convert the values to probability estimates in the range of 0.0 to 1.0

Reference:

J. Gao, P.-N. Tan
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates
Proc. Sixth International Conference on Data Mining, 2006. ICDM'06.

Since:

0.4.0

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
(package private) double	Afinal Sigmoid parameter
(package private) double	Bfinal Sigmoid parameter
private static Logging	LOG The logger for this class.

Constructor Summary

Constructors

Constructor and Description
SigmoidOutlierScaling()

Method Summary

Modifier and Type	Method and Description
double	getMax() Get maximum resulting value.
double	getMin() Get minimum resulting value.
double	getScaled(double value) Transform a given value using the scaling function.
private double[]	MStepLevenbergMarquardt(double a, double b, ArrayDBIDs ids, long[] t, DoubleRelation scores) M-Step using a modified Levenberg-Marquardt method.
private <A> double[]	MStepLevenbergMarquardt(double a, double b, long[] t, A array, NumberArrayAdapter<?,A> adapter) M-Step using a modified Levenberg-Marquardt method.
<A> void	prepare(A array, NumberArrayAdapter<?,A> adapter) Prepare is called once for each data set, before getScaled() will be called.
void	prepare(OutlierResult or) Prepare is called once for each data set, before getScaled() will be called.

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.

Afinal

double Afinal

Sigmoid parameter

Bfinal

double Bfinal

Sigmoid parameter

Constructor Detail

SigmoidOutlierScaling

public SigmoidOutlierScaling()

Method Detail

prepare

public void prepare(OutlierResult or)

Description copied from interface: OutlierScaling

Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the outlier scores.

Specified by:

prepare in interface OutlierScaling

Parameters:

or - Outlier result to use

prepare

public <A> void prepare(A array,
                        NumberArrayAdapter<?,A> adapter)

Description copied from interface: OutlierScaling

Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the score array. The method using a full OutlierResult is preferred, as it will allow access to the metadata.

Specified by:

prepare in interface OutlierScaling

Parameters:

array - Data to process

adapter - Array adapter

MStepLevenbergMarquardt

private double[] MStepLevenbergMarquardt(double a,
                                         double b,
                                         ArrayDBIDs ids,
                                         long[] t,
                                         DoubleRelation scores)

M-Step using a modified Levenberg-Marquardt method.

Implementation based on:
H.-T. Lin, C.-J. Lin, R. C. Weng:
A Note on Platt’s Probabilistic Outputs for Support Vector Machines

Parameters:

a - A parameter

b - B parameter

ids - Ids to process

t - Bitset containing the assignment

scores - Scores

Returns:

new values for A and B.

MStepLevenbergMarquardt

private <A> double[] MStepLevenbergMarquardt(double a,
                                             double b,
                                             long[] t,
                                             A array,
                                             NumberArrayAdapter<?,A> adapter)

M-Step using a modified Levenberg-Marquardt method.

Implementation based on:

H.-T. Lin, C.-J. Lin, R. C. Weng:
A Note on Platt’s Probabilistic Outputs for Support Vector Machines

Parameters:

a - A parameter

b - B parameter

t - Bitset containing the assignment

array - Score array

adapter - Array adapter

Returns:

new values for A and B.

getMax

public double getMax()

Description copied from interface: ScalingFunction

Get maximum resulting value. May be Double.NaN or Double.POSITIVE_INFINITY.

Specified by:

getMax in interface ScalingFunction

Returns:

Maximum resulting value.

getMin

public double getMin()

Description copied from interface: ScalingFunction

Get minimum resulting value. May be Double.NaN or Double.NEGATIVE_INFINITY.

Specified by:

getMin in interface ScalingFunction

Returns:

Minimum resulting value.

getScaled

public double getScaled(double value)

Description copied from interface: ScalingFunction

Transform a given value using the scaling function.

Specified by:

getScaled in interface ScalingFunction

Parameters:

value - Original value

Returns:

Scaled value