org.tribuo.transform.transformations

Class BinningTransformation

java.lang.Object

org.tribuo.transform.transformations.BinningTransformation

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TransformationProvenance>, Transformation

public final class BinningTransformation
extends Object
implements Transformation

A Transformation which bins values.

Three binning types are implemented:

• Equal width bins, based on the observed min and max.
• Equal frequency bins, based on the observed data.
• Standard deviation width bins, based on the observed standard deviation and mean.

The equal frequency TransformStatistics needs to store all the observed feature values, and thus has much higher memory usage than all other binning types.

The binned values are in the range [1, numBins].

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BinningTransformation.BinningTransformationProvenance Provenance for BinningTransformation.
static class	BinningTransformation.BinningType The allowed binning types.

Method Summary

Modifier and Type	Method and Description
TransformStatistics	createStats() Creates the statistics object for this Transformation.
static BinningTransformation	equalFrequency(int numBins) Returns a BinningTransformation which generates bins which contain the same amount of training data that is, each bin has an equal probability of occurrence in the training data.
static BinningTransformation	equalWidth(int numBins) Returns a BinningTransformation which generates fixed equal width bins between the observed min and max values.
TransformationProvenance	getProvenance()
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
static BinningTransformation	stdDevs(int numDeviations) Returns a BinningTransformation which generates bins based on the observed standard deviation of the training data.
String	toString()

Methods inherited from class java.lang.Object

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

Method Detail

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

createStats

public TransformStatistics createStats()

Description copied from interface: Transformation

Creates the statistics object for this Transformation.

Specified by:

createStats in interface Transformation

Returns:

The statistics object.

getProvenance

public TransformationProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TransformationProvenance>

toString

public String toString()

Overrides:

toString in class Object

equalWidth

public static BinningTransformation equalWidth(int numBins)

Returns a BinningTransformation which generates fixed equal width bins between the observed min and max values.

Values outside the observed range are clamped to either the minimum or maximum bin. Bins are numbered in the range [1,numBins].

Parameters:

numBins - The number of bins to generate.

Returns:

An equal width binning.

equalFrequency

public static BinningTransformation equalFrequency(int numBins)

Returns a BinningTransformation which generates bins which contain the same amount of training data that is, each bin has an equal probability of occurrence in the training data.

Values outside the observed range are clamped to either the minimum or maximum bin. Bins are numbered in the range [1,numBins].

Parameters:

numBins - The number of bins to generate.

Returns:

An equal frequency binning.

stdDevs

public static BinningTransformation stdDevs(int numDeviations)

Returns a BinningTransformation which generates bins based on the observed standard deviation of the training data. Each bin is a standard deviation wide, except for the bins at the edges which encompass all lower or higher values.

Bins are numbered in the range [1,numDeviations*2]. The middle two bins are either side of the mean, the lowest bin is the mean minus numDeviations * observed standard deviation, the highest bin is the mean plus numDeviations * observed standard deviation.

Parameters:

numDeviations - The number of standard deviations to bin.

Returns:

A standard deviation based binning.