Evaluation (nd4j-api 1.0.0-beta7 API)

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

All Implemented Interfaces:

Serializable, IEvaluation<Evaluation>
```
public class Evaluation
extends BaseEvaluation<Evaluation>
```
Evaluation metrics:
- precision, recall, f1, fBeta, accuracy, Matthews correlation coefficient, gMeasure
- Top N accuracy (if using constructor Evaluation(List, int))
- Custom binary evaluation decision threshold (use constructor Evaluation(double) (default if not set is argmax / 0.5)
- Custom cost array, using Evaluation(INDArray) or Evaluation(List, INDArray) for multi-class

Note: Care should be taken when using the Evaluation class for binary classification metrics such as F1, precision, recall, etc. There are a number of cases to consider:
1. For binary classification (1 or 2 network outputs)
a) Default behaviour: class 1 is assumed as the positive class. Consequently, no-arg methods such as f1(), precision(), recall() etc will report the binary metric for class 1 only
b) To set class 0 as the positive class instead of class 1 (the default), use Evaluation(int, Integer) or Evaluation(double, Integer) or #setBinaryPositiveClass(Integer). Then, f1(), precision(), recall() etc will report the binary metric for class 0 only.
c) To use macro-averaged metrics over both classes for binary classification (uncommon and usually not advisable) specify 'null' as the argument (instead of 0 or 1) as per (b) above
2. For multi-class classification, binary metric methods such as f1(), precision(), recall() will report macro-average (of the one-vs-all) binary metrics. Note that you can specify micro vs. macro averaging using f1(EvaluationAveraging) and similar methods

Note that setting a custom binary decision threshold is only possible for the binary case (1 or 2 outputs) and cannot be used if the number of classes exceeds 2. Predictions with probability > threshold are considered to be class 1, and are considered class 0 otherwise.

Cost arrays (a row vector, of size equal to the number of outputs) modify the evaluation process: instead of simply doing predictedClass = argMax(probabilities), we do predictedClass = argMax(cost * probabilities). Consequently, an array of all 1s (or, indeed any array of equal values) will result in the same performance as no cost array; non- equal values will bias the predictions for or against certain classes.

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class Evaluation.Metric

Nested Classes
Modifier and Type	Class and Description
`static class`	`Evaluation.Metric`

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`axis`
`protected Double`	`binaryDecisionThreshold`
`protected Integer`	`binaryPositiveClass`
`protected ConfusionMatrix<Integer>`	`confusion`
`protected static int`	`CONFUSION_PRINT_MAX_CLASSES`
`protected Map<Pair<Integer,Integer>,List<Object>>`	`confusionMatrixMetaData`
`protected INDArray`	`costArray`
`protected static double`	`DEFAULT_EDGE_VALUE`
`protected Counter<Integer>`	`falseNegatives`
`protected Counter<Integer>`	`falsePositives`
`protected List<String>`	`labelsList`
`protected int`	`maxWarningClassesToPrint` For stats(): When classes are excluded from precision/recall, what is the maximum number we should print? If this is set to a high value, the output (potentially thousands of classes) can become unreadable.
`protected int`	`numRowCounter`
`protected int`	`topN`
`protected int`	`topNCorrectCount`
`protected int`	`topNTotalCount`
`protected Counter<Integer>`	`trueNegatives`
`protected Counter<Integer>`	`truePositives`

Constructor Summary

Constructors
Modifier	Constructor and Description
	`Evaluation()`
	`Evaluation(double binaryDecisionThreshold)` Create an evaluation instance with a custom binary decision threshold.
	`Evaluation(double binaryDecisionThreshold, @NonNull Integer binaryPositiveClass)` Create an evaluation instance with a custom binary decision threshold.
	`Evaluation(INDArray costArray)` Created evaluation instance with the specified cost array.
	`Evaluation(int numClasses)` The number of classes to account for in the evaluation
	`Evaluation(int numClasses, Integer binaryPositiveClass)` Constructor for specifying the number of classes, and optionally the positive class for binary classification.
`protected`	`Evaluation(int axis, Integer binaryPositiveClass, int topN, List<String> labelsList, Double binaryDecisionThreshold, INDArray costArray, int maxWarningClassesToPrint)`
	`Evaluation(List<String> labels)` The labels to include with the evaluation.
	`Evaluation(List<String> labels, INDArray costArray)` Created evaluation instance with the specified cost array.
	`Evaluation(List<String> labels, int topN)` Constructor to use for top N accuracy
	`Evaluation(Map<Integer,String> labels)` Use a map to generate labels Pass in a label index with the actual label you want to use for output

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`accuracy()` Accuracy: (TP + TN) / (P + N)
`void`	`addToConfusion(Integer real, Integer guess)` Adds to the confusion matrix
`int`	`averageF1NumClassesExcluded()` When calculating the (macro) average F1, how many classes are excluded from the average due to no predictions - i.e., F1 would be calculated from a precision or recall of 0/0
`int`	`averageFBetaNumClassesExcluded()` When calculating the (macro) average FBeta, how many classes are excluded from the average due to no predictions - i.e., FBeta would be calculated from a precision or recall of 0/0
`int`	`averagePrecisionNumClassesExcluded()` When calculating the (macro) average precision, how many classes are excluded from the average due to no predictions - i.e., precision would be the edge case of 0/0
`int`	`averageRecallNumClassesExcluded()` When calculating the (macro) average Recall, how many classes are excluded from the average due to no predictions - i.e., recall would be the edge case of 0/0
`int`	`classCount(Integer clazz)` Returns the number of times the given label has actually occurred
`String`	`confusionMatrix()` Get the confusion matrix as a String
`String`	`confusionToString()` Get a String representation of the confusion matrix
`void`	`eval(INDArray realOutcomes, INDArray guesses)` Collects statistics on the real outcomes vs the guesses.
`void`	`eval(INDArray labels, INDArray predictions, INDArray mask, List<? extends Serializable> recordMetaData)` Evaluate the network, with optional metadata
`void`	`eval(int predictedIdx, int actualIdx)` Evaluate a single prediction (one prediction at a time)
`double`	`f1()` Calculate the F1 score F1 score is defined as: TP: true positive FP: False Positive FN: False Negative F1 score: 2 * TP / (2TP + FP + FN) Note: value returned will differ depending on number of classes and settings. 1.
`double`	`f1(EvaluationAveraging averaging)` Calculate the average F1 score across all classes, using macro or micro averaging
`double`	`f1(int classLabel)` Calculate f1 score for a given class
`double`	`falseAlarmRate()` False Alarm Rate (FAR) reflects rate of misclassified to classified records http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw Note: value returned will differ depending on number of classes and settings. 1.
`double`	`falseNegativeRate()` False negative rate based on guesses so far Note: value returned will differ depending on number of classes and settings. 1.
`double`	`falseNegativeRate(EvaluationAveraging averaging)` Calculate the average false negative rate for all classes - can specify whether macro or micro averaging should be used
`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
`Map<Integer,Integer>`	`falseNegatives()` False negatives: correctly rejected
`double`	`falsePositiveRate()` False positive rate based on guesses so far Note: value returned will differ depending on number of classes and settings. 1.
`double`	`falsePositiveRate(EvaluationAveraging averaging)` Calculate the average false positive rate across all classes.
`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
`Map<Integer,Integer>`	`falsePositives()` False positive: wrong guess
`double`	`fBeta(double beta, EvaluationAveraging averaging)` Calculate the average F_beta score across all classes, using macro or micro averaging
`double`	`fBeta(double beta, int classLabel)` Calculate the f_beta for a given class, where f_beta is defined as: (1+beta^2) * (precision * recall) / (beta^2 * precision + recall). F1 is a special case of f_beta, with beta=1.0
`double`	`fBeta(double beta, int classLabel, double defaultValue)` Calculate the f_beta for a given class, where f_beta is defined as: (1+beta^2) * (precision * recall) / (beta^2 * precision + recall). F1 is a special case of f_beta, with beta=1.0
`static Evaluation`	`fromJson(String json)`
`static Evaluation`	`fromYaml(String yaml)`
`int`	`getAxis()` Get the axis - see `setAxis(int)` for details
`String`	`getClassLabel(Integer clazz)`
`ConfusionMatrix<Integer>`	`getConfusionMatrix()` Returns the confusion matrix variable
`int`	`getNumRowCounter()`
`List<Prediction>`	`getPredictionByPredictedClass(int predictedClass)` Get a list of predictions, for all data with the specified predicted class, regardless of the actual data class.
`List<Prediction>`	`getPredictionErrors()` Get a list of prediction errors, on a per-record basis
`List<Prediction>`	`getPredictions(int actualClass, int predictedClass)` Get a list of predictions in the specified confusion matrix entry (i.e., for the given actua/predicted class pair)
`List<Prediction>`	`getPredictionsByActualClass(int actualClass)` Get a list of predictions, for all data with the specified actual class, regardless of the predicted class.
`int`	`getTopNCorrectCount()` Return the number of correct predictions according to top N value.
`int`	`getTopNTotalCount()` Return the total number of top N evaluations.
`double`	`getValue(IMetric metric)` Get the value of a given metric for this evaluation.
`double`	`gMeasure(EvaluationAveraging averaging)` Calculates the average G measure for all outputs using micro or macro averaging
`double`	`gMeasure(int output)` Calculate the G-measure for the given output
`void`	`incrementFalseNegatives(Integer classLabel)`
`void`	`incrementFalsePositives(Integer classLabel)`
`void`	`incrementTrueNegatives(Integer classLabel)`
`void`	`incrementTruePositives(Integer classLabel)`
`double`	`matthewsCorrelation(EvaluationAveraging averaging)` Calculate the average binary Mathews correlation coefficient, using macro or micro averaging. MCC = (TPTN - FPFN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN)) Note: This is NOT the same as the multi-class Matthews correlation coefficient
`double`	`matthewsCorrelation(int classIdx)` Calculate the binary Mathews correlation coefficient, for the specified class. MCC = (TPTN - FPFN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))
`void`	`merge(Evaluation other)` Merge the other evaluation object into this one.
`Map<Integer,Integer>`	`negative()` Total negatives true negatives + false negatives
`Evaluation`	`newInstance()` Get a new instance of this evaluation, with the same configuration but no data.
`protected int`	`numClasses()`
`Map<Integer,Integer>`	`positive()` Returns all of the positive guesses: true positive + false negative
`double`	`precision()` Precision based on guesses so far. Note: value returned will differ depending on number of classes and settings. 1.
`double`	`precision(EvaluationAveraging averaging)` Calculate the average precision for all classes.
`double`	`precision(Integer classLabel)` Returns the precision for a given class label
`double`	`precision(Integer classLabel, double edgeCase)` Returns the precision for a given label
`double`	`recall()` Recall based on guesses so far Note: value returned will differ depending on number of classes and settings. 1.
`double`	`recall(EvaluationAveraging averaging)` Calculate the average recall for all classes - can specify whether macro or micro averaging should be used NOTE: if any classes have tp=0 and fn=0, (recall=0/0) these are excluded from the average
`double`	`recall(int classLabel)` Returns the recall for a given label
`double`	`recall(int classLabel, double edgeCase)` Returns the recall for a given label
`void`	`reset()`
`double`	`scoreForMetric(Evaluation.Metric metric)`
`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()` Report the classification statistics as a String
`String`	`stats(boolean suppressWarnings)` Method to obtain the classification report as a String
`String`	`stats(boolean suppressWarnings, boolean includeConfusion)` Method to obtain the classification report as a String
`double`	`topNAccuracy()` Top N accuracy of the predictions so far.
`Map<Integer,Integer>`	`trueNegatives()` True negatives: correctly rejected
`Map<Integer,Integer>`	`truePositives()` True positives: correctly rejected

Methods inherited from class org.nd4j.evaluation.BaseEvaluation
attempFromLegacyFromJson, 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_EDGE_VALUE
```
protected static final double DEFAULT_EDGE_VALUE
```
    See Also:
    
    Constant Field Values
  - CONFUSION_PRINT_MAX_CLASSES
```
protected static final int CONFUSION_PRINT_MAX_CLASSES
```
    See Also:
    
    Constant Field Values
  - axis
```
protected int axis
```
  - binaryPositiveClass
```
protected Integer binaryPositiveClass
```
  - topN
```
protected final int topN
```
  - topNCorrectCount
```
protected int topNCorrectCount
```
  - topNTotalCount
```
protected int topNTotalCount
```
  - truePositives
```
protected Counter<Integer> truePositives
```
  - falsePositives
```
protected Counter<Integer> falsePositives
```
  - trueNegatives
```
protected Counter<Integer> trueNegatives
```
  - falseNegatives
```
protected Counter<Integer> falseNegatives
```
  - confusion
```
protected ConfusionMatrix<Integer> confusion
```
  - numRowCounter
```
protected int numRowCounter
```
  - labelsList
```
protected List<String> labelsList
```
  - binaryDecisionThreshold
```
protected Double binaryDecisionThreshold
```
  - costArray
```
protected INDArray costArray
```
  - confusionMatrixMetaData
```
protected Map<Pair<Integer,Integer>,List<Object>> confusionMatrixMetaData
```
  - maxWarningClassesToPrint
```
protected int maxWarningClassesToPrint
```
    For stats(): When classes are excluded from precision/recall, what is the maximum number we should print? If this is set to a high value, the output (potentially thousands of classes) can become unreadable.
- Constructor Detail
  - Evaluation
```
protected Evaluation(int axis,
                     Integer binaryPositiveClass,
                     int topN,
                     List<String> labelsList,
                     Double binaryDecisionThreshold,
                     INDArray costArray,
                     int maxWarningClassesToPrint)
```
  - Evaluation
```
public Evaluation()
```
  - Evaluation
```
public Evaluation(int numClasses)
```
    The number of classes to account for in the evaluation
    
    Parameters:
    
    numClasses - the number of classes to account for in the evaluation
  - Evaluation
```
public Evaluation(int numClasses,
                  Integer binaryPositiveClass)
```
    Constructor for specifying the number of classes, and optionally the positive class for binary classification. See Evaluation javadoc for more details on evaluation in the binary case
    
    Parameters:
    
    numClasses - The number of classes for the evaluation. Must be 2, if binaryPositiveClass is non-null
    
    binaryPositiveClass - If non-null, the positive class (0 or 1).
  - Evaluation
```
public Evaluation(List<String> labels)
```
    The labels to include with the evaluation. This constructor can be used for generating labeled output rather than just numbers for the labels
    
    Parameters:
    
    labels - the labels to use for the output
  - Evaluation
```
public Evaluation(Map<Integer,String> labels)
```
    Use a map to generate labels Pass in a label index with the actual label you want to use for output
    
    Parameters:
    
    labels - a map of label index to label value
  - Evaluation
```
public Evaluation(List<String> labels,
                  int topN)
```
    Constructor to use for top N accuracy
    
    Parameters:
    
    labels - Labels for the classes (may be null)
    
    topN - Value to use for top N accuracy calculation (<=1: standard accuracy). Note that with top N accuracy, an example is considered 'correct' if the probability for the true class is one of the highest N values
  - Evaluation
```
public Evaluation(double binaryDecisionThreshold)
```
    Create an evaluation instance with a custom binary decision threshold. Note that binary decision thresholds can only be used with binary classifiers.
    Defaults to class 1 for the positive class - see class javadoc, and use Evaluation(double, Integer) to change this.
    
    Parameters:
    
    binaryDecisionThreshold - Decision threshold to use for binary predictions
  - Evaluation
```
public Evaluation(double binaryDecisionThreshold,
                  @NonNull
                  @NonNull Integer binaryPositiveClass)
```
    Create an evaluation instance with a custom binary decision threshold. Note that binary decision thresholds can only be used with binary classifiers.
    This constructor also allows the user to specify the positive class for binary classification. See class javadoc for more details.
    
    Parameters:
    
    binaryDecisionThreshold - Decision threshold to use for binary predictions
  - Evaluation
```
public Evaluation(INDArray costArray)
```
    Created evaluation instance with the specified cost array. A cost array can be used to bias the multi class predictions towards or away from certain classes. The predicted class is determined using argMax(cost * probability) instead of argMax(probability) when no cost array is present.
    
    Parameters:
    
    costArray - Row vector cost array. May be null
  - Evaluation
```
public Evaluation(List<String> labels,
                  INDArray costArray)
```
    Created evaluation instance with the specified cost array. A cost array can be used to bias the multi class predictions towards or away from certain classes. The predicted class is determined using argMax(cost * probability) instead of argMax(probability) when no cost array is present.
    
    Parameters:
    
    labels - Labels for the output classes. May be null
    
    costArray - Row vector cost array. May be null
- Method Detail
  - numClasses
```
protected int numClasses()
```
  - reset
```
public void reset()
```
  - 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 realOutcomes,
                 INDArray guesses)
```
    Collects statistics on the real outcomes vs the guesses. This is for logistic outcome matrices.
    Note that an IllegalArgumentException is thrown if the two passed in matrices aren't the same length.
    
    Specified by:
    
    eval in interface IEvaluation<Evaluation>
    
    Overrides:
    
    eval in class BaseEvaluation<Evaluation>
    
    Parameters:
    
    realOutcomes - the real outcomes (labels - usually binary)
    
    guesses - the guesses/prediction (usually a probability vector)
  - 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
  - eval
```
public void eval(int predictedIdx,
                 int actualIdx)
```
    Evaluate a single prediction (one prediction at a time)
    
    Parameters:
    
    predictedIdx - Index of class predicted by the network
    
    actualIdx - Index of actual class
  - stats
```
public String stats()
```
    Report the classification statistics as a String
    
    Returns:
    
    Classification statistics as a String
  - stats
```
public String stats(boolean suppressWarnings)
```
    Method to obtain the classification report as a String
    
    Parameters:
    
    suppressWarnings - whether or not to output warnings related to the evaluation results
    
    Returns:
    
    A (multi-line) String with accuracy, precision, recall, f1 score etc
  - stats
```
public String stats(boolean suppressWarnings,
                    boolean includeConfusion)
```
    Method to obtain the classification report as a String
    
    Parameters:
    
    suppressWarnings - whether or not to output warnings related to the evaluation results
    
    includeConfusion - whether the confusion matrix should be included it the returned stats or not
    
    Returns:
    
    A (multi-line) String with accuracy, precision, recall, f1 score etc
  - confusionMatrix
```
public String confusionMatrix()
```
    Get the confusion matrix as a String
    
    Returns:
    
    Confusion matrix as a String
  - precision
```
public double precision(Integer classLabel)
```
    Returns the precision for a given class label
    
    Parameters:
    
    classLabel - the label
    
    Returns:
    
    the precision for the label
  - precision
```
public double precision(Integer classLabel,
                        double edgeCase)
```
    Returns the precision for a given label
    
    Parameters:
    
    classLabel - the label
    
    edgeCase - What to output in case of 0/0
    
    Returns:
    
    the precision for the label
  - precision
```
public double precision()
```
    Precision based on guesses so far.
    Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged precision, equivalent to precision(EvaluationAveraging.Macro)
    
    Returns:
    
    the total precision based on guesses so far
  - precision
```
public double precision(EvaluationAveraging averaging)
```
    Calculate the average precision for all classes. Can specify whether macro or micro averaging should be used NOTE: if any classes have tp=0 and fp=0, (precision=0/0) these are excluded from the average
    
    Parameters:
    
    averaging - Averaging method - macro or micro
    
    Returns:
    
    Average precision
  - averagePrecisionNumClassesExcluded
```
public int averagePrecisionNumClassesExcluded()
```
    When calculating the (macro) average precision, how many classes are excluded from the average due to no predictions - i.e., precision would be the edge case of 0/0
    
    Returns:
    
    Number of classes excluded from the average precision
  - averageRecallNumClassesExcluded
```
public int averageRecallNumClassesExcluded()
```
    When calculating the (macro) average Recall, how many classes are excluded from the average due to no predictions - i.e., recall would be the edge case of 0/0
    
    Returns:
    
    Number of classes excluded from the average recall
  - averageF1NumClassesExcluded
```
public int averageF1NumClassesExcluded()
```
    When calculating the (macro) average F1, how many classes are excluded from the average due to no predictions - i.e., F1 would be calculated from a precision or recall of 0/0
    
    Returns:
    
    Number of classes excluded from the average F1
  - averageFBetaNumClassesExcluded
```
public int averageFBetaNumClassesExcluded()
```
    When calculating the (macro) average FBeta, how many classes are excluded from the average due to no predictions - i.e., FBeta would be calculated from a precision or recall of 0/0
    
    Returns:
    
    Number of classes excluded from the average FBeta
  - recall
```
public double recall(int classLabel)
```
    Returns the recall for a given label
    
    Parameters:
    
    classLabel - the label
    
    Returns:
    
    Recall rate as a double
  - recall
```
public double recall(int classLabel,
                     double edgeCase)
```
    Returns the recall for a given label
    
    Parameters:
    
    classLabel - the label
    
    edgeCase - What to output in case of 0/0
    
    Returns:
    
    Recall rate as a double
  - recall
```
public double recall()
```
    Recall based on guesses so far
    Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged recall, equivalent to recall(EvaluationAveraging.Macro)
    
    Returns:
    
    the recall for the outcomes
  - recall
```
public double recall(EvaluationAveraging averaging)
```
    Calculate the average recall for all classes - can specify whether macro or micro averaging should be used NOTE: if any classes have tp=0 and fn=0, (recall=0/0) these are excluded from the average
    
    Parameters:
    
    averaging - Averaging method - macro or micro
    
    Returns:
    
    Average recall
  - 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
  - falsePositiveRate
```
public double falsePositiveRate()
```
    False positive rate based on guesses so far
    Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged false positive rate, equivalent to falsePositiveRate(EvaluationAveraging.Macro)
    
    Returns:
    
    the fpr for the outcomes
  - falsePositiveRate
```
public double falsePositiveRate(EvaluationAveraging averaging)
```
    Calculate the average false positive rate across all classes. Can specify whether macro or micro averaging should be used
    
    Parameters:
    
    averaging - Averaging method - macro or micro
    
    Returns:
    
    Average false positive rate
  - 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
  - falseNegativeRate
```
public double falseNegativeRate()
```
    False negative rate based on guesses so far Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged false negative rate, equivalent to falseNegativeRate(EvaluationAveraging.Macro)
    
    Returns:
    
    the fnr for the outcomes
  - falseNegativeRate
```
public double falseNegativeRate(EvaluationAveraging averaging)
```
    Calculate the average false negative rate for all classes - can specify whether macro or micro averaging should be used
    
    Parameters:
    
    averaging - Averaging method - macro or micro
    
    Returns:
    
    Average false negative rate
  - falseAlarmRate
```
public double falseAlarmRate()
```
    False Alarm Rate (FAR) reflects rate of misclassified to classified records http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw
    Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged false alarm rate)
    
    Returns:
    
    the fpr for the outcomes
  - f1
```
public double f1(int classLabel)
```
    Calculate f1 score for a given class
    
    Parameters:
    
    classLabel - the label to calculate f1 for
    
    Returns:
    
    the f1 score for the given label
  - fBeta
```
public double fBeta(double beta,
                    int classLabel)
```
    Calculate the f_beta for a given class, where f_beta is defined as:
    (1+beta^2) * (precision * recall) / (beta^2 * precision + recall).
    F1 is a special case of f_beta, with beta=1.0
    
    Parameters:
    
    beta - Beta value to use
    
    classLabel - Class label
    
    Returns:
    
    F_beta
  - fBeta
```
public double fBeta(double beta,
                    int classLabel,
                    double defaultValue)
```
    Calculate the f_beta for a given class, where f_beta is defined as:
    (1+beta^2) * (precision * recall) / (beta^2 * precision + recall).
    F1 is a special case of f_beta, with beta=1.0
    
    Parameters:
    
    beta - Beta value to use
    
    classLabel - Class label
    
    defaultValue - Default value to use when precision or recall is undefined (0/0 for prec. or recall)
    
    Returns:
    
    F_beta
  - f1
```
public double f1()
```
    Calculate the F1 score
    F1 score is defined as:
    TP: true positive
    FP: False Positive
    FN: False Negative
    F1 score: 2 * TP / (2TP + FP + FN)
    
    Note: value returned will differ depending on number of classes and settings.
    1. For binary classification, if the positive class is set (via default value of 1, via constructor, or via #setBinaryPositiveClass(Integer)), the returned value will be for the specified positive class only.
    2. For the multi-class case, or when #getBinaryPositiveClass() is null, the returned value is macro-averaged across all classes. i.e., is macro-averaged f1, equivalent to f1(EvaluationAveraging.Macro)
    
    Returns:
    
    the f1 score or harmonic mean of precision and recall based on current guesses
  - f1
```
public double f1(EvaluationAveraging averaging)
```
    Calculate the average F1 score across all classes, using macro or micro averaging
    
    Parameters:
    
    averaging - Averaging method to use
  - fBeta
```
public double fBeta(double beta,
                    EvaluationAveraging averaging)
```
    Calculate the average F_beta score across all classes, using macro or micro averaging
    
    Parameters:
    
    beta - Beta value to use
    
    averaging - Averaging method to use
  - gMeasure
```
public double gMeasure(int output)
```
    Calculate the G-measure for the given output
    
    Parameters:
    
    output - The specified output
    
    Returns:
    
    The G-measure for the specified output
  - gMeasure
```
public double gMeasure(EvaluationAveraging averaging)
```
    Calculates the average G measure for all outputs using micro or macro averaging
    
    Parameters:
    
    averaging - Averaging method to use
    
    Returns:
    
    Average G measure
  - accuracy
```
public double accuracy()
```
    Accuracy: (TP + TN) / (P + N)
    
    Returns:
    
    the accuracy of the guesses so far
  - topNAccuracy
```
public double topNAccuracy()
```
    Top N accuracy of the predictions so far. For top N = 1 (default), equivalent to accuracy()
    
    Returns:
    
    Top N accuracy
  - matthewsCorrelation
```
public double matthewsCorrelation(int classIdx)
```
    Calculate the binary Mathews correlation coefficient, for the specified class.
    MCC = (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))
    
    Parameters:
    
    classIdx - Class index to calculate Matthews correlation coefficient for
  - matthewsCorrelation
```
public double matthewsCorrelation(EvaluationAveraging averaging)
```
    Calculate the average binary Mathews correlation coefficient, using macro or micro averaging.
    MCC = (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))
    Note: This is NOT the same as the multi-class Matthews correlation coefficient
    
    Parameters:
    
    averaging - Averaging approach
    
    Returns:
    
    Average
  - truePositives
```
public Map<Integer,Integer> truePositives()
```
    True positives: correctly rejected
    
    Returns:
    
    the total true positives so far
  - trueNegatives
```
public Map<Integer,Integer> trueNegatives()
```
    True negatives: correctly rejected
    
    Returns:
    
    the total true negatives so far
  - falsePositives
```
public Map<Integer,Integer> falsePositives()
```
    False positive: wrong guess
    
    Returns:
    
    the count of the false positives
  - falseNegatives
```
public Map<Integer,Integer> falseNegatives()
```
    False negatives: correctly rejected
    
    Returns:
    
    the total false negatives so far
  - negative
```
public Map<Integer,Integer> negative()
```
    Total negatives true negatives + false negatives
    
    Returns:
    
    the overall negative count
  - positive
```
public Map<Integer,Integer> positive()
```
    Returns all of the positive guesses: true positive + false negative
  - incrementTruePositives
```
public void incrementTruePositives(Integer classLabel)
```
  - incrementTrueNegatives
```
public void incrementTrueNegatives(Integer classLabel)
```
  - incrementFalseNegatives
```
public void incrementFalseNegatives(Integer classLabel)
```
  - incrementFalsePositives
```
public void incrementFalsePositives(Integer classLabel)
```
  - addToConfusion
```
public void addToConfusion(Integer real,
                           Integer guess)
```
    Adds to the confusion matrix
    
    Parameters:
    
    real - the actual guess
    
    guess - the system guess
  - classCount
```
public int classCount(Integer clazz)
```
    Returns the number of times the given label has actually occurred
    
    Parameters:
    
    clazz - the label
    
    Returns:
    
    the number of times the label actually occurred
  - getNumRowCounter
```
public int getNumRowCounter()
```
  - getTopNCorrectCount
```
public int getTopNCorrectCount()
```
    Return the number of correct predictions according to top N value. For top N = 1 (default) this is equivalent to the number of correct predictions
    
    Returns:
    
    Number of correct top N predictions
  - getTopNTotalCount
```
public int getTopNTotalCount()
```
    Return the total number of top N evaluations. Most of the time, this is exactly equal to getNumRowCounter(), but may differ in the case of using eval(int, int) as top N accuracy cannot be calculated in that case (i.e., requires the full probability distribution, not just predicted/actual indices)
    
    Returns:
    
    Total number of top N predictions
  - getClassLabel
```
public String getClassLabel(Integer clazz)
```
  - getConfusionMatrix
```
public ConfusionMatrix<Integer> getConfusionMatrix()
```
    Returns the confusion matrix variable
    
    Returns:
    
    confusion matrix variable for this evaluation
  - merge
```
public void merge(Evaluation other)
```
    Merge the other evaluation object into this one. The result is that this Evaluation instance contains the counts etc from both
    
    Parameters:
    
    other - Evaluation object to merge into this one.
  - confusionToString
```
public String confusionToString()
```
    Get a String representation of the confusion matrix
  - getPredictionErrors
```
public List<Prediction> getPredictionErrors()
```
    Get a list of prediction errors, on a per-record basis
    
    Note: Prediction errors are ONLY available if the "evaluate with metadata" method is used: BaseEvaluation.eval(INDArray, INDArray, List) Otherwise (if the metadata hasn't been recorded via that previously mentioned eval method), there is no value in splitting each prediction out into a separate Prediction object - instead, use the confusion matrix to get the counts, via getConfusionMatrix()
    
    Returns:
    
    A list of prediction errors, or null if no metadata has been recorded
  - getPredictionsByActualClass
```
public List<Prediction> getPredictionsByActualClass(int actualClass)
```
    Get a list of predictions, for all data with the specified actual class, regardless of the predicted class.
    Note: Prediction errors are ONLY available if the "evaluate with metadata" method is used: BaseEvaluation.eval(INDArray, INDArray, List) Otherwise (if the metadata hasn't been recorded via that previously mentioned eval method), there is no value in splitting each prediction out into a separate Prediction object - instead, use the confusion matrix to get the counts, via getConfusionMatrix()
    
    Parameters:
    
    actualClass - Actual class to get predictions for
    
    Returns:
    
    List of predictions, or null if the "evaluate with metadata" method was not used
  - getPredictionByPredictedClass
```
public List<Prediction> getPredictionByPredictedClass(int predictedClass)
```
    Get a list of predictions, for all data with the specified predicted class, regardless of the actual data class.
    Note: Prediction errors are ONLY available if the "evaluate with metadata" method is used: BaseEvaluation.eval(INDArray, INDArray, List) Otherwise (if the metadata hasn't been recorded via that previously mentioned eval method), there is no value in splitting each prediction out into a separate Prediction object - instead, use the confusion matrix to get the counts, via getConfusionMatrix()
    
    Parameters:
    
    predictedClass - Actual class to get predictions for
    
    Returns:
    
    List of predictions, or null if the "evaluate with metadata" method was not used
  - getPredictions
```
public List<Prediction> getPredictions(int actualClass,
                                       int predictedClass)
```
    Get a list of predictions in the specified confusion matrix entry (i.e., for the given actua/predicted class pair)
    
    Parameters:
    
    actualClass - Actual class
    
    predictedClass - Predicted class
    
    Returns:
    
    List of predictions that match the specified actual/predicted classes, or null if the "evaluate with metadata" method was not used
  - scoreForMetric
```
public double scoreForMetric(Evaluation.Metric metric)
```
  - fromJson
```
public static Evaluation fromJson(String json)
```
  - fromYaml
```
public static Evaluation 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 Evaluation newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Class Evaluation

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_EDGE_VALUE

CONFUSION_PRINT_MAX_CLASSES

axis

binaryPositiveClass

topN

topNCorrectCount

topNTotalCount

truePositives

falsePositives

trueNegatives

falseNegatives

confusion

numRowCounter

labelsList

binaryDecisionThreshold

costArray

confusionMatrixMetaData

maxWarningClassesToPrint

Constructor Detail

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Evaluation

Method Detail

numClasses

reset

setAxis

getAxis

eval

eval

eval

stats

stats

stats

confusionMatrix

precision

precision

precision

precision

averagePrecisionNumClassesExcluded

averageRecallNumClassesExcluded

averageF1NumClassesExcluded

averageFBetaNumClassesExcluded

recall

recall

recall

recall

falsePositiveRate

falsePositiveRate

falsePositiveRate

falsePositiveRate

falseNegativeRate

falseNegativeRate

falseNegativeRate

falseNegativeRate

falseAlarmRate

f1

fBeta

fBeta

f1

f1

fBeta

gMeasure

gMeasure

accuracy