EvaluationCalibration (nd4j-api 1.0.0-M1 API)

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

All Implemented Interfaces:

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

Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

protected int axis

static int DEFAULT_HISTOGRAM_NUM_BINS

static int DEFAULT_RELIABILITY_DIAG_NUM_BINS

Fields
Modifier and Type	Field and Description
`protected int`	`axis`
`static int`	`DEFAULT_HISTOGRAM_NUM_BINS`
`static int`	`DEFAULT_RELIABILITY_DIAG_NUM_BINS`

Constructor Summary

Constructors
Modifier	Constructor and Description
	`EvaluationCalibration()` Create an EvaluationCalibration instance with the default number of bins
	`EvaluationCalibration(int reliabilityDiagNumBins, int histogramNumBins)` Create an EvaluationCalibration instance with the specified number of bins
	`EvaluationCalibration(int reliabilityDiagNumBins, int histogramNumBins, boolean excludeEmptyBins)` Create an EvaluationCalibration instance with the specified number of bins
`protected`	`EvaluationCalibration(int axis, int reliabilityDiagNumBins, int histogramNumBins, boolean excludeEmptyBins)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`eval(INDArray labels, INDArray networkPredictions)`
`void`	`eval(INDArray labels, INDArray predictions, INDArray mask)`
`void`	`eval(INDArray labels, INDArray networkPredictions, INDArray maskArray, List<? extends Serializable> recordMetaData)`
`static EvaluationCalibration`	`fromJson(String json)`
`int`	`getAxis()` Get the axis - see `setAxis(int)` for details
`int[]`	`getLabelCountsEachClass()`
`int[]`	`getPredictionCountsEachClass()`
`Histogram`	`getProbabilityHistogram(int labelClassIdx)` Return a probability histogram of the specified label class index.
`Histogram`	`getProbabilityHistogramAllClasses()` Return a probability histogram for all predictions/classes.
`ReliabilityDiagram`	`getReliabilityDiagram(int classIdx)` Get the reliability diagram for the specified class
`Histogram`	`getResidualPlot(int labelClassIdx)` Get the residual plot, only for examples of the specified class..
`Histogram`	`getResidualPlotAllClasses()` Get the residual plot for all classes combined.
`double`	`getValue(IMetric metric)` Get the value of a given metric for this evaluation.
`void`	`merge(EvaluationCalibration other)`
`EvaluationCalibration`	`newInstance()` Get a new instance of this evaluation, with the same configuration but no data.
`int`	`numClasses()`
`void`	`reset()`
`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()`

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_RELIABILITY_DIAG_NUM_BINS
```
public static final int DEFAULT_RELIABILITY_DIAG_NUM_BINS
```
    See Also:
    
    Constant Field Values
  - DEFAULT_HISTOGRAM_NUM_BINS
```
public static final int DEFAULT_HISTOGRAM_NUM_BINS
```
    See Also:
    
    Constant Field Values
  - axis
```
protected int axis
```
- Constructor Detail
  - EvaluationCalibration
```
protected EvaluationCalibration(int axis,
                                int reliabilityDiagNumBins,
                                int histogramNumBins,
                                boolean excludeEmptyBins)
```
  - EvaluationCalibration
```
public EvaluationCalibration()
```
    Create an EvaluationCalibration instance with the default number of bins
  - EvaluationCalibration
```
public EvaluationCalibration(int reliabilityDiagNumBins,
                             int histogramNumBins)
```
    Create an EvaluationCalibration instance with the specified number of bins
    
    Parameters:
    
    reliabilityDiagNumBins - Number of bins for the reliability diagram (usually 10)
    
    histogramNumBins - Number of bins for the histograms
  - EvaluationCalibration
```
public EvaluationCalibration(int reliabilityDiagNumBins,
                             int histogramNumBins,
                             boolean excludeEmptyBins)
```
    Create an EvaluationCalibration instance with the specified number of bins
    
    Parameters:
    
    reliabilityDiagNumBins - Number of bins for the reliability diagram (usually 10)
    
    histogramNumBins - Number of bins for the histograms
    
    excludeEmptyBins - For the reliability diagram, whether empty bins should be excluded
- 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 predictions,
                 INDArray mask)
```
    Specified by:
    
    eval in interface IEvaluation<EvaluationCalibration>
    
    Overrides:
    
    eval in class BaseEvaluation<EvaluationCalibration>
  - eval
```
public void eval(INDArray labels,
                 INDArray networkPredictions)
```
    Specified by:
    
    eval in interface IEvaluation<EvaluationCalibration>
    
    Overrides:
    
    eval in class BaseEvaluation<EvaluationCalibration>
  - eval
```
public void eval(INDArray labels,
                 INDArray networkPredictions,
                 INDArray maskArray,
                 List<? extends Serializable> recordMetaData)
```
  - merge
```
public void merge(EvaluationCalibration other)
```
  - reset
```
public void reset()
```
  - stats
```
public String stats()
```
    Returns:
  - numClasses
```
public int numClasses()
```
  - getReliabilityDiagram
```
public ReliabilityDiagram getReliabilityDiagram(int classIdx)
```
    Get the reliability diagram for the specified class
    
    Parameters:
    
    classIdx - Index of the class to get the reliability diagram for
  - getLabelCountsEachClass
```
public int[] getLabelCountsEachClass()
```
    Returns:
    
    The number of observed labels for each class. For N classes, be returned array is of length N, with out[i] being the number of labels of class i
  - getPredictionCountsEachClass
```
public int[] getPredictionCountsEachClass()
```
    Returns:
    
    The number of network predictions for each class. For N classes, be returned array is of length N, with out[i] being the number of predicted values (max probability) for class i
  - getResidualPlotAllClasses
```
public Histogram getResidualPlotAllClasses()
```
    Get the residual plot for all classes combined. The residual plot is defined as a histogram of
    |label_i - prob(class_i | input)| for all classes i and examples.
    In general, small residuals indicate a superior classifier to large residuals.
    
    Returns:
    
    Residual plot (histogram) - all predictions/classes
  - getResidualPlot
```
public Histogram getResidualPlot(int labelClassIdx)
```
    Get the residual plot, only for examples of the specified class.. The residual plot is defined as a histogram of
    |label_i - prob(class_i | input)| for all and examples; for this particular method, only predictions where i == labelClassIdx are included.
    In general, small residuals indicate a superior classifier to large residuals.
    
    Parameters:
    
    labelClassIdx - Index of the class to get the residual plot for
    
    Returns:
    
    Residual plot (histogram) - all predictions/classes
  - getProbabilityHistogramAllClasses
```
public Histogram getProbabilityHistogramAllClasses()
```
    Return a probability histogram for all predictions/classes.
    
    Returns:
    
    Probability histogram
  - getProbabilityHistogram
```
public Histogram getProbabilityHistogram(int labelClassIdx)
```
    Return a probability histogram of the specified label class index. That is, for label class index i, a histogram of P(class_i | input) is returned, only for those examples that are labelled as class i.
    
    Parameters:
    
    labelClassIdx - Index of the label class to get the histogram for
    
    Returns:
    
    Probability histogram
  - fromJson
```
public static EvaluationCalibration fromJson(String json)
```
  - getValue
```
public double getValue(IMetric metric)
```
    Description copied from interface: IEvaluation
    
    Get the value of a given metric for this evaluation.
  - newInstance
```
public EvaluationCalibration newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Class EvaluationCalibration

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.nd4j.evaluation.BaseEvaluation

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_RELIABILITY_DIAG_NUM_BINS

DEFAULT_HISTOGRAM_NUM_BINS

axis

Constructor Detail

EvaluationCalibration

EvaluationCalibration

EvaluationCalibration

EvaluationCalibration

Method Detail

setAxis

getAxis

eval

eval

eval

merge

reset

stats

numClasses

getReliabilityDiagram

getLabelCountsEachClass

getPredictionCountsEachClass

getResidualPlotAllClasses

getResidualPlot

getProbabilityHistogramAllClasses

getProbabilityHistogram

fromJson

getValue

newInstance