EvaluationBinary

java.lang.Object
- org.deeplearning4j.eval.BaseEvaluation<EvaluationBinary>
- - org.deeplearning4j.eval.EvaluationBinary

All Implemented Interfaces:

java.io.Serializable, IEvaluation<EvaluationBinary>
```
public class EvaluationBinary
extends BaseEvaluation<EvaluationBinary>
```
EvaluationBinary: used for evaluating networks with binary classification outputs. The typical classification metrics, such as accuracy, precision, recall, F1 score, etc. are calculated for each output.
Note that ROCBinary is also used internally to calculate AUC for each output, but only when using an appropriate constructor, EvaluationBinary(int, Integer)
Note that EvaluationBinary supports both per-example and per-output masking.
The most common use case: multi-task networks, where each output is a binary value. This differs from Evaluation in that Evaluation is for a single class (binary or non-binary) evaluation.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static int DEFAULT_PRECISION

Fields
Modifier and Type	Field and Description
`static int`	`DEFAULT_PRECISION`

Constructor Summary

Constructors
Constructor and Description
`EvaluationBinary(int size)`
`EvaluationBinary(int size, java.lang.Integer rocBinarySteps)` This constructor allows for ROC to be calculated in addition to the standard evaluation metrics, when the rocBinarySteps arg is non-null.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`accuracy(int outputNum)` Get the accuracy for the specified output
`void`	`eval(org.nd4j.linalg.api.ndarray.INDArray labels, org.nd4j.linalg.api.ndarray.INDArray networkPredictions)`
`void`	`eval(org.nd4j.linalg.api.ndarray.INDArray labels, org.nd4j.linalg.api.ndarray.INDArray networkPredictions, org.nd4j.linalg.api.ndarray.INDArray maskArray)`
`void`	`evalTimeSeries(org.nd4j.linalg.api.ndarray.INDArray labels, org.nd4j.linalg.api.ndarray.INDArray predictions, org.nd4j.linalg.api.ndarray.INDArray labelsMask)`
`double`	`f1(int outputNum)` Get the F1 score for the specified output
`int`	`falseNegatives(int outputNum)` Get the false negatives count for the specified output
`int`	`falsePositives(int outputNum)` Get the false positives count for the specified output
`ROCBinary`	`getROCBinary()` Returns the `ROCBinary` instance, if present
`void`	`merge(EvaluationBinary other)`
`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`	`setLabelNames(java.util.List<java.lang.String> labels)` Set the label names, for printing via `stats()`
`java.lang.String`	`stats()` Get a String representation of the EvaluationBinary class, using the default precision
`java.lang.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.deeplearning4j.eval.BaseEvaluation
eval, evalTimeSeries

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

- Field Detail
  - DEFAULT_PRECISION
```
public static final int DEFAULT_PRECISION
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - EvaluationBinary
```
public EvaluationBinary(int size)
```
  - EvaluationBinary
```
public EvaluationBinary(int size,
                        java.lang.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 - Consructor arg for ROCBinary.ROCBinary(int)
- Method Detail
  - eval
```
public void eval(org.nd4j.linalg.api.ndarray.INDArray labels,
                 org.nd4j.linalg.api.ndarray.INDArray networkPredictions)
```
  - evalTimeSeries
```
public void evalTimeSeries(org.nd4j.linalg.api.ndarray.INDArray labels,
                           org.nd4j.linalg.api.ndarray.INDArray predictions,
                           org.nd4j.linalg.api.ndarray.INDArray labelsMask)
```
    Specified by:
    
    evalTimeSeries in interface IEvaluation<EvaluationBinary>
    
    Overrides:
    
    evalTimeSeries in class BaseEvaluation<EvaluationBinary>
  - eval
```
public void eval(org.nd4j.linalg.api.ndarray.INDArray labels,
                 org.nd4j.linalg.api.ndarray.INDArray networkPredictions,
                 org.nd4j.linalg.api.ndarray.INDArray maskArray)
```
    Specified by:
    
    eval in interface IEvaluation<EvaluationBinary>
    
    Overrides:
    
    eval in class BaseEvaluation<EvaluationBinary>
  - merge
```
public void merge(EvaluationBinary other)
```
  - 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(java.util.List<java.lang.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
  - accuracy
```
public double accuracy(int outputNum)
```
    Get the accuracy for the specified output
  - precision
```
public double precision(int outputNum)
```
    Get the precision (tp / (tp + fp)) for the specified output
  - recall
```
public double recall(int outputNum)
```
    Get the recall (tp / (tp + fn)) for the specified output
  - f1
```
public double f1(int outputNum)
```
    Get the F1 score for the specified output
  - getROCBinary
```
public ROCBinary getROCBinary()
```
    Returns the ROCBinary instance, if present
  - stats
```
public java.lang.String stats()
```
    Get a String representation of the EvaluationBinary class, using the default precision
  - stats
```
public java.lang.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.

Class EvaluationBinary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.deeplearning4j.eval.BaseEvaluation

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_PRECISION

Constructor Detail

EvaluationBinary

EvaluationBinary

Method Detail

eval

evalTimeSeries

eval

merge

numLabels

setLabelNames

totalCount

truePositives

trueNegatives

falsePositives

falseNegatives

accuracy

precision

recall

f1

getROCBinary

stats

stats