ClassPerplexity (entropy-triangle-weka-package)

java.lang.Object
- weka.classifiers.evaluation.AbstractEvaluationMetric
- - weka.etplugin.metrics.ClassPerplexity

All Implemented Interfaces:

Serializable, InformationTheoreticEvaluationMetric
```
public class ClassPerplexity
extends AbstractEvaluationMetric
implements InformationTheoreticEvaluationMetric
```
Information theoretic evaluation metric for classifiers. The class perplexity can be interpreted as the number of effective classes at the input of the classifier due to the likelihood of the input distribution. This is defined as \[ PP_X = 2^{H_X} \] where \( H_X \) is the entropy of the likelihood of the classes. \( PP_X = k \), the number of classes, when \( X \) is uniform. In general \(1 \leq PP_X \leq k \)

For more information, see

Valverde-Albacete, F. J., & Peláez-Moreno, C. (2014). 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox. PLoS ONE 9(1).

NOTE: This class needs to have an associated Evaluation object to compute the metric. The Evaluation object can be set via AbstractEvaluationMetric.setBaseEvaluation(Evaluation eval).
If the package is properly installed in Weka, a reference to the object of this class associated with every Evaluation object can be obtained via the method Evaluation.getPluginMetric(String metricName), or the method Evaluation.getPluginMetrics() to get a list of all the plugin metrics associated with that Evaluation object.

Author:

Antonio Pastor

See Also:
Evaluation, Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from class weka.classifiers.evaluation.AbstractEvaluationMetric
  AbstractEvaluationMetric.UnknownStatisticException

Field Summary

Fields
Modifier and Type Field and Description

static String METRIC_NAME
Constant string with the metric name

Fields
Modifier and Type	Field and Description
`static String`	`METRIC_NAME` Constant string with the metric name

Constructor Summary

Constructors
Constructor and Description

ClassPerplexity()

Constructors
Constructor and Description
`ClassPerplexity()`

Method Summary

Methods
Modifier and Type	Method and Description
`boolean`	`appliesToNominalClass()` Returns true.
`boolean`	`appliesToNumericClass()` Returns false.
`String`	`getMetricDescription()` Get a short description of this metric.
`String`	`getMetricName()` Get the name of this metric.
`double`	`getStatistic(String statName)` Get the value of the named statistic, should be "Class perplexity".
`List<String>`	`getStatisticNames()` Get a list with the name of the metric.
`boolean`	`statisticIsMaximisable(String statName)` Returns true.
`String`	`toSummaryString()` Returns a formatted string (suitable for displaying in console or GUI output) containing this metric.
`void`	`updateStatsForClassifier(double[] predictedDistribution, Instance instance)` Not used.
`void`	`updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier, Instance classMissing, double classValue)` Not used.
`void`	`updateStatsForPredictor(double predictedValue, Instance instance)` Not used.

Methods inherited from class weka.classifiers.evaluation.AbstractEvaluationMetric
getPluginMetrics, setBaseEvaluation

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

- Field Detail
  - METRIC_NAME
```
public static String METRIC_NAME
```
    Constant string with the metric name
- Constructor Detail
  - ClassPerplexity
```
public ClassPerplexity()
```
- Method Detail
  - toSummaryString
```
public String toSummaryString()
```
    Returns a formatted string (suitable for displaying in console or GUI output) containing this metric.
    
    Specified by:
    
    toSummaryString in interface InformationTheoreticEvaluationMetric
    
    Returns:
    a formatted string containing the metric
  - appliesToNominalClass
```
public boolean appliesToNominalClass()
```
    Returns true.
    
    Specified by:
    
    appliesToNominalClass in class AbstractEvaluationMetric
  - appliesToNumericClass
```
public boolean appliesToNumericClass()
```
    Returns false.
    
    Specified by:
    
    appliesToNumericClass in class AbstractEvaluationMetric
  - statisticIsMaximisable
```
public boolean statisticIsMaximisable(String statName)
```
    Returns true. The maximum possible value is the number of classes if the classes distribution of the dataset is uniform. The minimum possible value is 1, for a dataset where all the instances are of the same class.
    
    Overrides:
    
    statisticIsMaximisable in class AbstractEvaluationMetric
  - getMetricName
```
public String getMetricName()
```
    Get the name of this metric.
    
    Specified by:
    
    getMetricName in class AbstractEvaluationMetric
    
    Returns:
    the name of this metric
  - getMetricDescription
```
public String getMetricDescription()
```
    Get a short description of this metric.
    
    Specified by:
    
    getMetricDescription in class AbstractEvaluationMetric
    
    Returns:
    a short description of this metric
  - getStatisticNames
```
public List<String> getStatisticNames()
```
    Get a list with the name of the metric.
    
    Specified by:
    
    getStatisticNames in class AbstractEvaluationMetric
    
    Returns:
    the names of the metric
  - getStatistic
```
public double getStatistic(String statName)
```
    Get the value of the named statistic, should be "Class perplexity".
    
    Specified by:
    
    getStatistic in class AbstractEvaluationMetric
    
    Parameters:
    statName - the only valid statistic is "Class perplexity"
    
    Returns:
    the computed statistic
    
    Throws:
    
    AbstractEvaluationMetric.UnknownStatisticException - if the statistic name is not "Class perplexity"
  - updateStatsForClassifier
```
public void updateStatsForClassifier(double[] predictedDistribution,
                            Instance instance)
                              throws Exception
```
    Not used. This method is required to conform to the InformationTheoreticEvaluationMetric interface, but not implemented.
    
    Specified by:
    
    updateStatsForClassifier in interface InformationTheoreticEvaluationMetric
    
    Throws:
    
    Exception
  - updateStatsForPredictor
```
public void updateStatsForPredictor(double predictedValue,
                           Instance instance)
                             throws Exception
```
    Not used. This method is required to conform to the InformationTheoreticEvaluationMetric interface, but not implemented.
    
    Specified by:
    
    updateStatsForPredictor in interface InformationTheoreticEvaluationMetric
    
    Throws:
    
    Exception
  - updateStatsForConditionalDensityEstimator
```
public void updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier,
                                             Instance classMissing,
                                             double classValue)
                                               throws Exception
```
    Not used. This method is required to conform to the InformationTheoreticEvaluationMetric interface, but not implemented.
    
    Specified by:
    
    updateStatsForConditionalDensityEstimator in interface InformationTheoreticEvaluationMetric
    
    Throws:
    
    Exception

Class ClassPerplexity

Nested Class Summary

Nested classes/interfaces inherited from class weka.classifiers.evaluation.AbstractEvaluationMetric

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.evaluation.AbstractEvaluationMetric

Methods inherited from class java.lang.Object

Field Detail

METRIC_NAME

Constructor Detail

ClassPerplexity

Method Detail

toSummaryString

appliesToNominalClass

appliesToNumericClass

statisticIsMaximisable

getMetricName

getMetricDescription

getStatisticNames

getStatistic

updateStatsForClassifier

updateStatsForPredictor

updateStatsForConditionalDensityEstimator