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

All Implemented Interfaces:

Serializable, IEvaluation<EvaluationBinary>
```
public class EvaluationBinary
extends BaseEvaluation<EvaluationBinary>
```
See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class EvaluationBinary.Metric

Field Summary

Fields
Modifier and Type Field Description

protected int axis

static double DEFAULT_EDGE_VALUE

static int DEFAULT_PRECISION

Constructor Summary

Constructors
Modifier	Constructor	Description
	`EvaluationBinary(int size, Integer rocBinarySteps)`	This constructor allows for ROC to be calculated in addition to the standard evaluation metrics, when the rocBinarySteps arg is non-null.
`protected`	`EvaluationBinary(int axis, ROCBinary rocBinary, List<String> labels, INDArray decisionThreshold)`
	`EvaluationBinary(INDArray decisionThreshold)`	Create an EvaulationBinary instance with an optional decision threshold array.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`accuracy(int outputNum)`	Get the accuracy for the specified output
`double`	`averageAccuracy()`
`double`	`averageF1()`
`double`	`averageFalseAlarmRate()`	Average False Alarm Rate (FAR) (see `falseAlarmRate(int)`) for all labels.
`double`	`averageGMeasure()`	Average G-measure (see `gMeasure(int)`) for all labels.
`double`	`averageMatthewsCorrelation()`	Macro average of the Matthews correlation coefficient (MCC) (see `matthewsCorrelation(int)`) for all labels.
`double`	`averagePrecision()`
`double`	`averageRecall()`
`void`	`eval(INDArray labels, INDArray networkPredictions)`
`void`	`eval(INDArray labelsArr, INDArray predictionsArr, INDArray maskArr)`
`void`	`eval(INDArray labels, INDArray networkPredictions, INDArray maskArray, List<? extends Serializable> recordMetaData)`
`double`	`f1(int outputNum)`	Get the F1 score for the specified output
`double`	`falseAlarmRate(int outputNum)`	False Alarm Rate (FAR) reflects rate of misclassified to classified records http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw
`double`	`falseNegativeRate(Integer classLabel)`	Returns the false negative rate for a given label
`double`	`falseNegativeRate(Integer classLabel, double edgeCase)`	Returns the false negative rate for a given label
`int`	`falseNegatives(int outputNum)`	Get the false negatives count for the specified output
`double`	`falsePositiveRate(int classLabel)`	Returns the false positive rate for a given label
`double`	`falsePositiveRate(int classLabel, double edgeCase)`	Returns the false positive rate for a given label
`int`	`falsePositives(int outputNum)`	Get the false positives count for the specified output
`double`	`fBeta(double beta, int outputNum)`	Calculate the F-beta value for the given output
`static EvaluationBinary`	`fromJson(String json)`
`static EvaluationBinary`	`fromYaml(String yaml)`
`int`	`getAxis()`	Get the axis - see `setAxis(int)` for details
`ROCBinary`	`getROCBinary()`	Returns the `ROCBinary` instance, if present
`double`	`getValue(IMetric metric)`	Get the value of a given metric for this evaluation.
`double`	`gMeasure(int output)`	Calculate the macro average G-measure for the given output
`double`	`matthewsCorrelation(int outputNum)`	Calculate the Matthews correlation coefficient for the specified output
`void`	`merge(EvaluationBinary other)`	Merge the other evaluation object into this one.
`EvaluationBinary`	`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.
`double`	`precision(int outputNum)`	Get the precision (tp / (tp + fp)) for the specified output
`double`	`recall(int outputNum)`	Get the recall (tp / (tp + fn)) for the specified output
`void`	`reset()`
`double`	`scoreForMetric(EvaluationBinary.Metric metric, int outputNum)`	Calculate specific metric (see `EvaluationBinary.Metric`) for a given label.
`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
`void`	`setLabelNames(List<String> labels)`	Set the label names, for printing via `stats()`
`String`	`stats()`	Get a String representation of the EvaluationBinary class, using the default precision
`String`	`stats(int printPrecision)`	Get a String representation of the EvaluationBinary class, using the specified precision
`int`	`totalCount(int outputNum)`	Get the total number of values for the specified column, accounting for any masking
`int`	`trueNegatives(int outputNum)`	Get the true negatives count for the specified output
`int`	`truePositives(int outputNum)`	Get the true positives count for the specified output

Methods inherited from class org.nd4j.evaluation.BaseEvaluation
attempFromLegacyFromJson, 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_PRECISION
```
public static final int DEFAULT_PRECISION
```
    See Also:
    
    Constant Field Values
  - DEFAULT_EDGE_VALUE
```
public static final double DEFAULT_EDGE_VALUE
```
    See Also:
    
    Constant Field Values
  - axis
```
protected int axis
```
- Constructor Detail
  - EvaluationBinary
```
protected EvaluationBinary(int axis,
                           ROCBinary rocBinary,
                           List<String> labels,
                           INDArray decisionThreshold)
```
  - EvaluationBinary
```
public EvaluationBinary(INDArray decisionThreshold)
```
    Create an EvaulationBinary instance with an optional decision threshold array.
    
    Parameters:
    
    decisionThreshold - Decision threshold for each output; may be null. Should be a row vector with length equal to the number of outputs, with values in range 0 to 1. An array of 0.5 values is equivalent to the default (no manually specified decision threshold).
  - EvaluationBinary
```
public EvaluationBinary(int size,
                        Integer rocBinarySteps)
```
    This constructor allows for ROC to be calculated in addition to the standard evaluation metrics, when the rocBinarySteps arg is non-null. See ROCBinary for more details
    
    Parameters:
    
    size - Number of outputs
    
    rocBinarySteps - Constructor arg for ROCBinary(int)
- 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
  - eval
```
public void eval(INDArray labels,
                 INDArray networkPredictions)
```
    Specified by:
    
    eval in interface IEvaluation<EvaluationBinary>
    
    Overrides:
    
    eval in class BaseEvaluation<EvaluationBinary>
  - eval
```
public void eval(INDArray labels,
                 INDArray networkPredictions,
                 INDArray maskArray,
                 List<? extends Serializable> recordMetaData)
```
  - eval
```
public void eval(INDArray labelsArr,
                 INDArray predictionsArr,
                 INDArray maskArr)
```
    Specified by:
    
    eval in interface IEvaluation<EvaluationBinary>
    
    Overrides:
    
    eval in class BaseEvaluation<EvaluationBinary>
  - merge
```
public void merge(EvaluationBinary other)
```
    Merge the other evaluation object into this one. The result is that this EvaluationBinary(int, java.lang.Integer) instance contains the counts etc from both
    
    Parameters:
    
    other - EvaluationBinary object to merge into this one.
  - reset
```
public void reset()
```
  - numLabels
```
public int numLabels()
```
    Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known. Returns -1 otherwise
  - setLabelNames
```
public void setLabelNames(List<String> labels)
```
    Set the label names, for printing via stats()
  - totalCount
```
public int totalCount(int outputNum)
```
    Get the total number of values for the specified column, accounting for any masking
  - truePositives
```
public int truePositives(int outputNum)
```
    Get the true positives count for the specified output
  - trueNegatives
```
public int trueNegatives(int outputNum)
```
    Get the true negatives count for the specified output
  - falsePositives
```
public int falsePositives(int outputNum)
```
    Get the false positives count for the specified output
  - falseNegatives
```
public int falseNegatives(int outputNum)
```
    Get the false negatives count for the specified output
  - averageAccuracy
```
public double averageAccuracy()
```
  - accuracy
```
public double accuracy(int outputNum)
```
    Get the accuracy for the specified output
  - averagePrecision
```
public double averagePrecision()
```
  - precision
```
public double precision(int outputNum)
```
    Get the precision (tp / (tp + fp)) for the specified output
  - averageRecall
```
public double averageRecall()
```
  - recall
```
public double recall(int outputNum)
```
    Get the recall (tp / (tp + fn)) for the specified output
  - averageF1
```
public double averageF1()
```
  - fBeta
```
public double fBeta(double beta,
                    int outputNum)
```
    Calculate the F-beta value for the given output
    
    Parameters:
    
    beta - Beta value to use
    
    outputNum - Output number
    
    Returns:
    
    F-beta for the given output
  - f1
```
public double f1(int outputNum)
```
    Get the F1 score for the specified output
  - matthewsCorrelation
```
public double matthewsCorrelation(int outputNum)
```
    Calculate the Matthews correlation coefficient for the specified output
    
    Parameters:
    
    outputNum - Output number
    
    Returns:
    
    Matthews correlation coefficient
  - averageMatthewsCorrelation
```
public double averageMatthewsCorrelation()
```
    Macro average of the Matthews correlation coefficient (MCC) (see matthewsCorrelation(int)) for all labels.
    
    Returns:
    
    The macro average of the MCC for all labels.
  - gMeasure
```
public double gMeasure(int output)
```
    Calculate the macro average G-measure for the given output
    
    Parameters:
    
    output - The specified output
    
    Returns:
    
    The macro average of the G-measure for the specified output
  - averageGMeasure
```
public double averageGMeasure()
```
    Average G-measure (see gMeasure(int)) for all labels.
    
    Returns:
    
    The G-measure for all labels.
  - falsePositiveRate
```
public double falsePositiveRate(int classLabel)
```
    Returns the false positive rate for a given label
    
    Parameters:
    
    classLabel - the label
    
    Returns:
    
    fpr as a double
  - falsePositiveRate
```
public double falsePositiveRate(int classLabel,
                                double edgeCase)
```
    Returns the false positive rate for a given label
    
    Parameters:
    
    classLabel - the label
    
    edgeCase - What to output in case of 0/0
    
    Returns:
    
    fpr as a double
  - falseNegativeRate
```
public double falseNegativeRate(Integer classLabel)
```
    Returns the false negative rate for a given label
    
    Parameters:
    
    classLabel - the label
    
    Returns:
    
    fnr as a double
  - falseNegativeRate
```
public double falseNegativeRate(Integer classLabel,
                                double edgeCase)
```
    Returns the false negative rate for a given label
    
    Parameters:
    
    classLabel - the label
    
    edgeCase - What to output in case of 0/0
    
    Returns:
    
    fnr as a double
  - getROCBinary
```
public ROCBinary getROCBinary()
```
    Returns the ROCBinary instance, if present
  - averageFalseAlarmRate
```
public double averageFalseAlarmRate()
```
    Average False Alarm Rate (FAR) (see falseAlarmRate(int)) for all labels.
    
    Returns:
    
    The FAR for all labels.
  - falseAlarmRate
```
public double falseAlarmRate(int outputNum)
```
    False Alarm Rate (FAR) reflects rate of misclassified to classified records http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw
    
    Parameters:
    
    outputNum - Class index to calculate False Alarm Rate (FAR)
    
    Returns:
    
    The FAR for the outcomes
  - stats
```
public String stats()
```
    Get a String representation of the EvaluationBinary class, using the default precision
    
    Returns:
  - stats
```
public String stats(int printPrecision)
```
    Get a String representation of the EvaluationBinary class, using the specified precision
    
    Parameters:
    
    printPrecision - The precision (number of decimal places) for the accuracy, f1, etc.
  - scoreForMetric
```
public double scoreForMetric(EvaluationBinary.Metric metric,
                             int outputNum)
```
    Calculate specific metric (see EvaluationBinary.Metric) for a given label.
    
    Parameters:
    
    metric - The Metric to calculate.
    
    outputNum - Class index to calculate.
    
    Returns:
    
    Calculated metric.
  - fromJson
```
public static EvaluationBinary fromJson(String json)
```
  - fromYaml
```
public static EvaluationBinary fromYaml(String yaml)
```
  - getValue
```
public double getValue(IMetric metric)
```
    Description copied from interface: IEvaluation
    
    Get the value of a given metric for this evaluation.
  - newInstance
```
public EvaluationBinary newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Modifier and Type	Field	Description
`protected int`	`axis`
`static double`	`DEFAULT_EDGE_VALUE`
`static int`	`DEFAULT_PRECISION`

Class EvaluationBinary

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_PRECISION

DEFAULT_EDGE_VALUE

axis

Constructor Detail

EvaluationBinary

EvaluationBinary

EvaluationBinary

Method Detail

setAxis

getAxis

eval

eval

eval

merge

reset

numLabels

setLabelNames

totalCount

truePositives

trueNegatives

falsePositives

falseNegatives

averageAccuracy

accuracy

averagePrecision

precision

averageRecall

recall

averageF1

fBeta

f1

matthewsCorrelation

averageMatthewsCorrelation

gMeasure

averageGMeasure

falsePositiveRate

falsePositiveRate

falseNegativeRate

falseNegativeRate

getROCBinary

averageFalseAlarmRate

falseAlarmRate

stats

stats

scoreForMetric

fromJson

fromYaml

getValue

newInstance