ROCBinary (nd4j-api 1.0.0-beta6 API)

java.lang.Object
- org.nd4j.evaluation.BaseEvaluation<ROCBinary>
- - org.nd4j.evaluation.classification.ROCBinary

All Implemented Interfaces:

Serializable, IEvaluation<ROCBinary>
```
public class ROCBinary
extends BaseEvaluation<ROCBinary>
```
ROC (Receiver Operating Characteristic) for multi-task binary classifiers. As per ROC, ROCBinary supports both exact (thersholdSteps == 0) and thresholded; see ROC for details.
Unlike ROC (which supports a single binary label (as a single column probability, or 2 column 'softmax' probability distribution), ROCBinary assumes that all outputs are independent binary variables. This also differs from ROCMultiClass, which should be used for multi-class (single non-binary) cases.
ROCBinary supports per-example and per-output masking: for per-output masking, any particular output may be absent (mask value 0) and hence won't be included in the calculated ROC.

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class ROCBinary.Metric
AUROC: Area under ROC curve
AUPRC: Area under Precision-Recall Curve

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

Field Summary

Fields
Modifier and Type Field and Description

protected int axis

static int DEFAULT_STATS_PRECISION

Fields
Modifier and Type	Field and Description
`protected int`	`axis`
`static int`	`DEFAULT_STATS_PRECISION`

Constructor Summary

Constructors
Modifier	Constructor and Description
	`ROCBinary()`
	`ROCBinary(int thresholdSteps)`
	`ROCBinary(int thresholdSteps, boolean rocRemoveRedundantPts)`
`protected`	`ROCBinary(int axis, int thresholdSteps, boolean rocRemoveRedundantPts, List<String> labels)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`calculateAUC(int outputNum)` Calculate the AUC - Area Under (ROC) Curve Utilizes trapezoidal integration internally
`double`	`calculateAUCPR(int outputNum)` Calculate the AUCPR - Area Under Curve - Precision Recall Utilizes trapezoidal integration internally
`double`	`calculateAverageAuc()` Macro-average AUC for all outcomes
`double`	`calculateAverageAUCPR()`
`void`	`eval(INDArray labels, INDArray predictions, INDArray mask, List<? extends Serializable> recordMetaData)`
`static ROCBinary`	`fromJson(String json)`
`int`	`getAxis()` Get the axis - see `setAxis(int)` for details
`long`	`getCountActualNegative(int outputNum)` Get the actual negative count (accounting for any masking) for the specified output/column
`long`	`getCountActualPositive(int outputNum)` Get the actual positive count (accounting for any masking) for the specified output/column
`PrecisionRecallCurve`	`getPrecisionRecallCurve(int outputNum)` Get the Precision-Recall curve for the specified output
`ROC`	`getROC(int outputNum)` Get the ROC object for the specific column
`RocCurve`	`getRocCurve(int outputNum)` Get the ROC curve for the specified output
`double`	`getValue(IMetric metric)` Get the value of a given metric for this evaluation.
`void`	`merge(ROCBinary other)`
`ROCBinary`	`newInstance()` Get a new instance of this evaluation, with the same configuration but no data.
`int`	`numLabels()` Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known.
`void`	`reset()`
`double`	`scoreForMetric(ROCBinary.Metric metric, int idx)`
`void`	`setAxis(int axis)` Set the axis for evaluation - this is the dimension along which the probability (and label independent binary classes) are present. 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
`void`	`setLabelNames(List<String> labels)` Set the label names, for printing via `stats()`
`String`	`stats()`
`String`	`stats(int printPrecision)`

Methods inherited from class org.nd4j.evaluation.BaseEvaluation
attempFromLegacyFromJson, eval, eval, eval, evalTimeSeries, evalTimeSeries, fromJson, fromYaml, reshapeAndExtractNotMasked, toJson, toString, toYaml

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - DEFAULT_STATS_PRECISION
```
public static final int DEFAULT_STATS_PRECISION
```
    See Also:
    
    Constant Field Values
  - axis
```
protected int axis
```
- Constructor Detail
  - ROCBinary
```
protected ROCBinary(int axis,
                    int thresholdSteps,
                    boolean rocRemoveRedundantPts,
                    List<String> labels)
```
  - ROCBinary
```
public ROCBinary()
```
  - ROCBinary
```
public ROCBinary(int thresholdSteps)
```
    Parameters:
    
    thresholdSteps - Number of threshold steps to use for the ROC calculation. Set to 0 for exact ROC calculation
  - ROCBinary
```
public ROCBinary(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
- Method Detail
  - setAxis
```
public void setAxis(int axis)
```
    Set the axis for evaluation - this is the dimension along which the probability (and label independent binary classes) are present.
    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
  - reset
```
public void reset()
```
  - eval
```
public void eval(INDArray labels,
                 INDArray predictions,
                 INDArray mask,
                 List<? extends Serializable> recordMetaData)
```
  - merge
```
public void merge(ROCBinary other)
```
  - numLabels
```
public int numLabels()
```
    Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known. Returns -1 otherwise
  - getCountActualPositive
```
public long getCountActualPositive(int outputNum)
```
    Get the actual positive count (accounting for any masking) for the specified output/column
    
    Parameters:
    
    outputNum - Index of the output (0 to numLabels()-1)
  - getCountActualNegative
```
public long getCountActualNegative(int outputNum)
```
    Get the actual negative count (accounting for any masking) for the specified output/column
    
    Parameters:
    
    outputNum - Index of the output (0 to numLabels()-1)
  - getROC
```
public ROC getROC(int outputNum)
```
    Get the ROC object for the specific column
    
    Parameters:
    
    outputNum - Column (output number)
    
    Returns:
    
    The underlying ROC object for this specific column
  - getRocCurve
```
public RocCurve getRocCurve(int outputNum)
```
    Get the ROC curve for the specified output
    
    Parameters:
    
    outputNum - Number of the output to get the ROC curve for
    
    Returns:
    
    ROC curve
  - getPrecisionRecallCurve
```
public PrecisionRecallCurve getPrecisionRecallCurve(int outputNum)
```
    Get the Precision-Recall curve for the specified output
    
    Parameters:
    
    outputNum - Number of the output to get the P-R curve for
    
    Returns:
    
    Precision recall curve
  - calculateAverageAuc
```
public double calculateAverageAuc()
```
    Macro-average AUC for all outcomes
    
    Returns:
    
    the (macro-)average AUC for all outcomes.
  - calculateAverageAUCPR
```
public double calculateAverageAUCPR()
```
    Returns:
    
    the (macro-)average AUPRC (area under precision recall curve)
  - calculateAUC
```
public double calculateAUC(int outputNum)
```
    Calculate the AUC - Area Under (ROC) Curve
    Utilizes trapezoidal integration internally
    
    Parameters:
    
    outputNum - Output number to calculate AUC for
    
    Returns:
    
    AUC
  - calculateAUCPR
```
public double calculateAUCPR(int outputNum)
```
    Calculate the AUCPR - Area Under Curve - Precision Recall
    Utilizes trapezoidal integration internally
    
    Parameters:
    
    outputNum - Output number to calculate AUCPR for
    
    Returns:
    
    AUCPR
  - setLabelNames
```
public void setLabelNames(List<String> labels)
```
    Set the label names, for printing via stats()
  - stats
```
public String stats()
```
    Returns:
  - stats
```
public String stats(int printPrecision)
```
  - fromJson
```
public static ROCBinary fromJson(String json)
```
  - scoreForMetric
```
public double scoreForMetric(ROCBinary.Metric metric,
                             int idx)
```
  - getValue
```
public double getValue(IMetric metric)
```
    Description copied from interface: IEvaluation
    
    Get the value of a given metric for this evaluation.
  - newInstance
```
public ROCBinary newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Class ROCBinary

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.nd4j.evaluation.BaseEvaluation

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_STATS_PRECISION

axis

Constructor Detail

ROCBinary

ROCBinary

ROCBinary

ROCBinary

Method Detail

setAxis

getAxis

reset

eval

merge

numLabels

getCountActualPositive

getCountActualNegative

getROC

getRocCurve

getPrecisionRecallCurve

calculateAverageAuc

calculateAverageAUCPR

calculateAUC

calculateAUCPR

setLabelNames

stats

stats

fromJson

scoreForMetric

getValue

newInstance