MultiLabelEvaluationImpl (Tribuo 4.1.1 API)

java.lang.Object
- org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<EvaluationProvenance>, ClassifierEvaluation<MultiLabel>, Evaluation<MultiLabel>, MultiLabelEvaluation
```
public final class MultiLabelEvaluationImpl
extends Object
implements MultiLabelEvaluation
```
The implementation of a MultiLabelEvaluation using the default metrics.
The classification metrics consider labels independently.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Map<MetricID<MultiLabel>,Double>`	`asMap()` Get a map of all the metrics stored in this evaluation.
`double`	`balancedErrorRate()` Returns the balanced error rate, i.e., the mean of the per label recalls.
`double`	`confusion(MultiLabel predicted, MultiLabel truth)` Returns the number of times label `truth` was predicted as label `predicted`.
`double`	`f1(MultiLabel label)` Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
`double`	`fn()` Returns the micro averaged number of false negatives.
`double`	`fn(MultiLabel label)` Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
`double`	`fp()` Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
`double`	`fp(MultiLabel label)` Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
`double`	`get(MetricID<MultiLabel> key)` Gets the value associated with the specific metric.
`ConfusionMatrix<MultiLabel>`	`getConfusionMatrix()` Returns the underlying confusion matrix.
`List<Prediction<MultiLabel>>`	`getPredictions()` Gets the predictions stored in this evaluation.
`EvaluationProvenance`	`getProvenance()`
`double`	`jaccardScore()` The average across the predictions of the intersection of the true and predicted labels divided by the union of the true and predicted labels.
`double`	`macroAveragedF1()` Returns the macro averaged F_1 across all the labels.
`double`	`macroAveragedPrecision()` Returns the macro averaged precision.
`double`	`macroAveragedRecall()` Returns the macro averaged recall.
`double`	`macroFN()` Returns the macro averaged number of false negatives.
`double`	`macroFP()` Returns the macro averaged number of false positives, averaged across the labels.
`double`	`macroTN()` Returns the macro averaged number of true negatives.
`double`	`macroTP()` Returns the macro averaged number of true positives, averaged across the labels.
`double`	`microAveragedF1()` Returns the micro averaged F_1 across all labels.
`double`	`microAveragedPrecision()` Returns the micro averaged precision.
`double`	`microAveragedRecall()` Returns the micro averaged recall.
`double`	`precision(MultiLabel label)` Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
`double`	`recall(MultiLabel label)` Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
`double`	`tn()` Returns the total number of true negatives.
`double`	`tn(MultiLabel label)` Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
`String`	`toString()`
`double`	`tp()` Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
`double`	`tp(MultiLabel label)` Returns the number of true positives, i.e., the number of times the label was correctly predicted.

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

- Method Detail
  - getPredictions
```
public List<Prediction<MultiLabel>> getPredictions()
```
    Description copied from interface: Evaluation
    
    Gets the predictions stored in this evaluation.
    
    Specified by:
    
    getPredictions in interface Evaluation<MultiLabel>
    
    Returns:
    
    The predictions.
  - balancedErrorRate
```
public double balancedErrorRate()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the balanced error rate, i.e., the mean of the per label recalls.
    
    Specified by:
    
    balancedErrorRate in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The balanced error rate.
  - getConfusionMatrix
```
public ConfusionMatrix<MultiLabel> getConfusionMatrix()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the underlying confusion matrix.
    
    Specified by:
    
    getConfusionMatrix in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The confusion matrix.
  - confusion
```
public double confusion(MultiLabel predicted,
                        MultiLabel truth)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the number of times label truth was predicted as label predicted.
    
    Specified by:
    
    confusion in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    predicted - The predicted label.
    
    truth - The true label.
    
    Returns:
    
    The number of times the predicted label was returned for the true label.
  - tp
```
public double tp(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the number of true positives, i.e., the number of times the label was correctly predicted.
    
    Specified by:
    
    tp in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The label to calculate.
    
    Returns:
    
    The number of true positives for that label.
  - tp
```
public double tp()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
    
    Specified by:
    
    tp in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The micro averaged number of true positives.
  - macroTP
```
public double macroTP()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged number of true positives, averaged across the labels.
    
    Specified by:
    
    macroTP in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged number of true positives.
  - fp
```
public double fp(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
    
    Specified by:
    
    fp in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - the label to calculate.
    
    Returns:
    
    The number of false positives for that label.
  - fp
```
public double fp()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
    
    Specified by:
    
    fp in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The micro averaged number of false positives.
  - macroFP
```
public double macroFP()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged number of false positives, averaged across the labels.
    
    Specified by:
    
    macroFP in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged number of false positives.
  - tn
```
public double tn(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
    
    Specified by:
    
    tn in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The label to use.
    
    Returns:
    
    the number of true negatives.
  - tn
```
public double tn()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the total number of true negatives. This isn't very useful in multiclass problems.
    
    Specified by:
    
    tn in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The number of true negatives.
  - macroTN
```
public double macroTN()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged number of true negatives.
    
    Specified by:
    
    macroTN in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged number of true negatives.
  - fn
```
public double fn(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
    
    Specified by:
    
    fn in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The true label.
    
    Returns:
    
    The number of false negatives.
  - fn
```
public double fn()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro averaged number of false negatives.
    
    Specified by:
    
    fn in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The micro averaged number of false negatives.
  - macroFN
```
public double macroFN()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged number of false negatives.
    
    Specified by:
    
    macroFN in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged number of false negatives.
  - precision
```
public double precision(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
    
    Specified by:
    
    precision in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The precision.
  - microAveragedPrecision
```
public double microAveragedPrecision()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro averaged precision.
    
    Specified by:
    
    microAveragedPrecision in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The micro averaged precision.
  - macroAveragedPrecision
```
public double macroAveragedPrecision()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged precision.
    
    Specified by:
    
    macroAveragedPrecision in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged precision.
  - recall
```
public double recall(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
    
    Specified by:
    
    recall in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The recall.
  - microAveragedRecall
```
public double microAveragedRecall()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro averaged recall.
    
    Specified by:
    
    microAveragedRecall in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The micro averaged recall.
  - macroAveragedRecall
```
public double macroAveragedRecall()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged recall.
    
    Specified by:
    
    macroAveragedRecall in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The macro averaged recall.
  - f1
```
public double f1(MultiLabel label)
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
    
    Specified by:
    
    f1 in interface ClassifierEvaluation<MultiLabel>
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The F_1 score.
  - microAveragedF1
```
public double microAveragedF1()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the micro averaged F_1 across all labels.
    
    Specified by:
    
    microAveragedF1 in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The F_1 score.
  - macroAveragedF1
```
public double macroAveragedF1()
```
    Description copied from interface: ClassifierEvaluation
    
    Returns the macro averaged F_1 across all the labels.
    
    Specified by:
    
    macroAveragedF1 in interface ClassifierEvaluation<MultiLabel>
    
    Returns:
    
    The F_1 score.
  - jaccardScore
```
public double jaccardScore()
```
    Description copied from interface: MultiLabelEvaluation
    
    The average across the predictions of the intersection of the true and predicted labels divided by the union of the true and predicted labels.
    
    Specified by:
    
    jaccardScore in interface MultiLabelEvaluation
    
    Returns:
    
    The Jaccard score.
  - asMap
```
public Map<MetricID<MultiLabel>,Double> asMap()
```
    Description copied from interface: Evaluation
    
    Get a map of all the metrics stored in this evaluation. The keys are metric id's and the values are their corresponding computed results.
    
    Specified by:
    
    asMap in interface Evaluation<MultiLabel>
    
    Returns:
    
    a map of all stored results
  - getProvenance
```
public EvaluationProvenance getProvenance()
```
    Specified by:
    
    getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<EvaluationProvenance>
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - get
```
public double get(MetricID<MultiLabel> key)
```
    Description copied from interface: Evaluation
    
    Gets the value associated with the specific metric. Throws IllegalArgumentException if the metric is unknown.
    
    Specified by:
    
    get in interface Evaluation<MultiLabel>
    
    Parameters:
    
    key - The metric to lookup.
    
    Returns:
    
    The value for that metric.

Class MultiLabelEvaluationImpl

Method Summary

Methods inherited from class java.lang.Object

Method Detail

getPredictions

balancedErrorRate

getConfusionMatrix

confusion

tp

tp

macroTP

fp

fp

macroFP

tn

tn

macroTN

fn

fn

macroFN

precision

microAveragedPrecision

macroAveragedPrecision

recall

microAveragedRecall

macroAveragedRecall

f1

microAveragedF1

macroAveragedF1

jaccardScore

asMap

getProvenance

toString

get