ROCMultiClass (nd4j-api 1.0.0-beta5 API)

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

All Implemented Interfaces:

Serializable, IEvaluation<ROCMultiClass>
```
public class ROCMultiClass
extends BaseEvaluation<ROCMultiClass>
```
ROC (Receiver Operating Characteristic) for multi-class classifiers. As per ROC, ROCBinary supports both exact (thersholdSteps == 0) and thresholded; see ROC for details.
The ROC curves are produced by treating the predictions as a set of one-vs-all classifiers, and then calculating ROC curves for each. In practice, this means for N classes, we get N ROC curves.

Author:

Alex Black

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

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

Nested Classes
Modifier and Type	Class and Description
`static class`	`ROCMultiClass.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
	`ROCMultiClass()`
	`ROCMultiClass(int thresholdSteps)`
	`ROCMultiClass(int thresholdSteps, boolean rocRemoveRedundantPts)`
`protected`	`ROCMultiClass(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 classIdx)` Calculate the AUC - Area Under ROC Curve Utilizes trapezoidal integration internally
`double`	`calculateAUCPR(int classIdx)` Calculate the AUPRC - Area Under Curve Precision Recall Utilizes trapezoidal integration internally
`double`	`calculateAverageAUC()` Calculate the macro-average (one-vs-all) AUC for all classes
`double`	`calculateAverageAUCPR()` Calculate the macro-average (one-vs-all) AUCPR (area under precision recall curve) for all classes
`void`	`eval(INDArray labels, INDArray predictions, INDArray mask, List<? extends Serializable> recordMetaData)` Evaluate the network, with optional metadata
`static ROCMultiClass`	`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 class
`int`	`getNumClasses()`
`PrecisionRecallCurve`	`getPrecisionRecallCurve(int classIdx)` Get the (one vs.
`RocCurve`	`getRocCurve(int classIdx)` Get the (one vs.
`double`	`getValue(IMetric metric)` Get the value of a given metric for this evaluation.
`void`	`merge(ROCMultiClass other)` Merge this ROCMultiClass instance with another.
`ROCMultiClass`	`newInstance()` Get a new instance of this evaluation, with the same configuration but no data.
`void`	`reset()`
`double`	`scoreForMetric(ROCMultiClass.Metric metric, int idx)`
`void`	`setAxis(int axis)` Set the axis for evaluation - this is the dimension along which the probability (and label 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
`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
  - ROCMultiClass
```
protected ROCMultiClass(int axis,
                        int thresholdSteps,
                        boolean rocRemoveRedundantPts,
                        List<String> labels)
```
  - ROCMultiClass
```
public ROCMultiClass()
```
  - ROCMultiClass
```
public ROCMultiClass(int thresholdSteps)
```
    Parameters:
    
    thresholdSteps - Number of threshold steps to use for the ROC calculation. Set to 0 for exact ROC calculation
  - ROCMultiClass
```
public ROCMultiClass(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 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()
```
  - stats
```
public String stats()
```
    Returns:
  - stats
```
public String stats(int printPrecision)
```
  - eval
```
public void eval(INDArray labels,
                 INDArray predictions,
                 INDArray mask,
                 List<? extends Serializable> recordMetaData)
```
    Evaluate the network, with optional metadata
    
    Parameters:
    
    labels - Data labels
    
    predictions - Network predictions
    
    recordMetaData - Optional; may be null. If not null, should have size equal to the number of outcomes/guesses
  - getRocCurve
```
public RocCurve getRocCurve(int classIdx)
```
    Get the (one vs. all) ROC curve for the specified class
    
    Parameters:
    
    classIdx - Class index to get the ROC curve for
    
    Returns:
    
    ROC curve for the given class
  - getPrecisionRecallCurve
```
public PrecisionRecallCurve getPrecisionRecallCurve(int classIdx)
```
    Get the (one vs. all) Precision-Recall curve for the specified class
    
    Parameters:
    
    classIdx - Class to get the P-R curve for
    
    Returns:
    
    Precision recall curve for the given class
  - calculateAUC
```
public double calculateAUC(int classIdx)
```
    Calculate the AUC - Area Under ROC Curve
    Utilizes trapezoidal integration internally
    
    Returns:
    
    AUC
  - calculateAUCPR
```
public double calculateAUCPR(int classIdx)
```
    Calculate the AUPRC - Area Under Curve Precision Recall
    Utilizes trapezoidal integration internally
    
    Returns:
    
    AUC
  - calculateAverageAUC
```
public double calculateAverageAUC()
```
    Calculate the macro-average (one-vs-all) AUC for all classes
  - calculateAverageAUCPR
```
public double calculateAverageAUCPR()
```
    Calculate the macro-average (one-vs-all) AUCPR (area under precision recall curve) for all classes
  - getCountActualPositive
```
public long getCountActualPositive(int outputNum)
```
    Get the actual positive count (accounting for any masking) for the specified class
    
    Parameters:
    
    outputNum - Index of the class
  - 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 class
  - merge
```
public void merge(ROCMultiClass other)
```
    Merge this ROCMultiClass instance with another. This ROCMultiClass instance is modified, by adding the stats from the other instance.
    
    Parameters:
    
    other - ROCMultiClass instance to combine with this one
  - getNumClasses
```
public int getNumClasses()
```
  - fromJson
```
public static ROCMultiClass fromJson(String json)
```
  - scoreForMetric
```
public double scoreForMetric(ROCMultiClass.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 ROCMultiClass newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Class ROCMultiClass

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

ROCMultiClass

ROCMultiClass

ROCMultiClass

ROCMultiClass

Method Detail

setAxis

getAxis

reset

stats

stats

eval

getRocCurve

getPrecisionRecallCurve

calculateAUC

calculateAUCPR

calculateAverageAUC

calculateAverageAUCPR

getCountActualPositive

getCountActualNegative

merge

getNumClasses

fromJson

scoreForMetric

getValue

newInstance