Package org.nd4j.evaluation.classification

Class ROC

java.lang.Object

org.nd4j.evaluation.BaseEvaluation<ROC>

org.nd4j.evaluation.classification.ROC

All Implemented Interfaces:

Serializable, IEvaluation<ROC>

public class ROC
extends BaseEvaluation<ROC>

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	ROC.CountsForThreshold
static class	ROC.Metric	AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve

Field Summary

Fields

Modifier and Type	Field	Description
protected int	axis

Constructor Summary

Constructors

Constructor	Description
ROC()
ROC(int thresholdSteps)
ROC(int thresholdSteps, boolean rocRemoveRedundantPts)
ROC(int thresholdSteps, boolean rocRemoveRedundantPts, int exactAllocBlockSize)
ROC(int thresholdSteps, boolean rocRemoveRedundantPts, int exactAllocBlockSize, int axis)

Method Summary

Modifier and Type	Method	Description
double	calculateAUC()	Calculate the AUROC - Area Under ROC Curve Utilizes trapezoidal integration internally
double	calculateAUCPR()	Calculate the area under the precision/recall curve - aka AUCPR
void	eval(INDArray labels, INDArray predictions, INDArray mask, List<? extends Serializable> recordMetaData)	Evaluate (collect statistics for) the given minibatch of data.
static ROC	fromJson(String json)
int	getAxis()	Get the axis - see setAxis(int) for details
PrecisionRecallCurve	getPrecisionRecallCurve()	Get the precision recall curve as array.
protected INDArray	getProbAndLabelUsed()
RocCurve	getRocCurve()	Get the ROC curve, as a set of (threshold, falsePositive, truePositive) points
double	getValue(IMetric metric)	Get the value of a given metric for this evaluation.
void	merge(ROC other)	Merge this ROC instance with another.
ROC	newInstance()	Get a new instance of this evaluation, with the same configuration but no data.
void	reset()
double	scoreForMetric(ROC.Metric metric)
void	setAxis(int axis)	Set the axis for evaluation - this should be a size 1 dimension For DL4J, this can be left as the default setting (axis = 1). Axis should be set as follows: For 2D (OutputLayer), shape [minibatch, numClasses] - axis = 1 For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NCW format, shape [minibatch, numClasses, sequenceLength] - axis = 1 For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NWC format, shape [minibatch, sequenceLength, numClasses] - axis = 2 For 4D, CNN2D (DL4J CnnLossLayer), NCHW format, shape [minibatch, channels, height, width] - axis = 1 For 4D, CNN2D, NHWC format, shape [minibatch, height, width, channels] - axis = 3
String	stats()
String	toString()

Methods inherited from class org.nd4j.evaluation.BaseEvaluation

attempFromLegacyFromJson, eval, eval, eval, evalTimeSeries, evalTimeSeries, fromJson, fromYaml, reshapeAndExtractNotMasked, toJson, toYaml

Methods inherited from class java.lang.Object

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

Field Detail

axis

protected int axis

Constructor Detail

ROC

public ROC(int thresholdSteps,
           boolean rocRemoveRedundantPts,
           int exactAllocBlockSize,
           int axis)

ROC

public ROC()

ROC

public ROC(int thresholdSteps)

Parameters:

thresholdSteps - Number of threshold steps to use for the ROC calculation. If set to 0: use exact calculation

ROC

public ROC(int thresholdSteps,
           boolean rocRemoveRedundantPts)

Parameters:

thresholdSteps - Number of threshold steps to use for the ROC calculation. If set to 0: use exact calculation

rocRemoveRedundantPts - Usually set to true. If true, remove any redundant points from ROC and P-R curves

ROC

public ROC(int thresholdSteps,
           boolean rocRemoveRedundantPts,
           int exactAllocBlockSize)

Parameters:

thresholdSteps - Number of threshold steps to use for the ROC calculation. If set to 0: use exact calculation

rocRemoveRedundantPts - Usually set to true. If true, remove any redundant points from ROC and P-R curves

exactAllocBlockSize - if using exact mode, the block size relocation. Users can likely use the default setting in almost all cases

Method Detail

fromJson

public static ROC fromJson(String json)

setAxis

public void setAxis(int axis)

Set the axis for evaluation - this should be a size 1 dimension For DL4J, this can be left as the default setting (axis = 1).
Axis should be set as follows:
For 2D (OutputLayer), shape [minibatch, numClasses] - axis = 1
For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NCW format, shape [minibatch, numClasses, sequenceLength] - axis = 1
For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NWC format, shape [minibatch, sequenceLength, numClasses] - axis = 2
For 4D, CNN2D (DL4J CnnLossLayer), NCHW format, shape [minibatch, channels, height, width] - axis = 1
For 4D, CNN2D, NHWC format, shape [minibatch, height, width, channels] - axis = 3

Parameters:

axis - Axis to use for evaluation

getAxis

public int getAxis()

Get the axis - see setAxis(int) for details

calculateAUC

public double calculateAUC()

Calculate the AUROC - Area Under ROC Curve
Utilizes trapezoidal integration internally

Returns:

AUC

getRocCurve

public RocCurve getRocCurve()

Get the ROC curve, as a set of (threshold, falsePositive, truePositive) points

Returns:

ROC curve

getProbAndLabelUsed

protected INDArray getProbAndLabelUsed()

calculateAUCPR

public double calculateAUCPR()

Calculate the area under the precision/recall curve - aka AUCPR

Returns:

getPrecisionRecallCurve

public PrecisionRecallCurve getPrecisionRecallCurve()

Get the precision recall curve as array. return[0] = threshold array
return[1] = precision array
return[2] = recall array

Returns:

eval

public void eval(INDArray labels,
                 INDArray predictions,
                 INDArray mask,
                 List<? extends Serializable> recordMetaData)

Evaluate (collect statistics for) the given minibatch of data. For time series (3 dimensions) use BaseEvaluation.evalTimeSeries(INDArray, INDArray) or BaseEvaluation.evalTimeSeries(INDArray, INDArray, INDArray)

Parameters:

labels - Labels / true outcomes

predictions - Predictions

merge

public void merge(ROC other)

Merge this ROC instance with another. This ROC instance is modified, by adding the stats from the other instance.

Parameters:

other - ROC instance to combine with this one

reset

public void reset()

stats

public String stats()

Returns:

toString

public String toString()

Overrides:

toString in class BaseEvaluation<ROC>

scoreForMetric

public double scoreForMetric(ROC.Metric metric)

getValue

public double getValue(IMetric metric)

Description copied from interface: IEvaluation

Get the value of a given metric for this evaluation.

newInstance

public ROC newInstance()

Description copied from interface: IEvaluation

Get a new instance of this evaluation, with the same configuration but no data.