ThresholdCurve (weka-stable 3.6.14 API)

java.lang.Object
- weka.classifiers.evaluation.ThresholdCurve

All Implemented Interfaces:

RevisionHandler
```
public class ThresholdCurve
extends Object
implements RevisionHandler
```
Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes. For example, the typical threshold value of 0.5 means the predicted probability of "positive" must be higher than 0.5 for the instance to be predicted as "positive". The resulting dataset can be used to visualize precision/recall tradeoff, or for ROC curve analysis (true positive rate vs false positive rate). Weka just varies the threshold on the class probability estimates in each case. The Mann Whitney statistic is used to calculate the AUC.

Version:

$Revision: 7833 $

Author:

Len Trigg (len@reeltwo.com)

Field Summary

Fields
Modifier and Type	Field and Description
`static String`	`FALLOUT_NAME` attribute name: Fallout
`static String`	`FALSE_NEG_NAME` attribute name: False Negatives
`static String`	`FALSE_POS_NAME` attribute name: False Positives
`static String`	`FMEASURE_NAME` attribute name: FMeasure
`static String`	`FP_RATE_NAME` attribute name: False Positive Rate"
`static String`	`LIFT_NAME` attribute name: Lift
`static String`	`PRECISION_NAME` attribute name: Precision
`static String`	`RECALL_NAME` attribute name: Recall
`static String`	`RELATION_NAME` The name of the relation used in threshold curve datasets
`static String`	`SAMPLE_SIZE_NAME` attribute name: Sample Size
`static String`	`THRESHOLD_NAME` attribute name: Threshold
`static String`	`TP_RATE_NAME` attribute name: True Positive Rate
`static String`	`TRUE_NEG_NAME` attribute name: True Negatives
`static String`	`TRUE_POS_NAME` attribute name: True Positives

Constructor Summary

Constructors
Constructor and Description

ThresholdCurve()

Constructors
Constructor and Description
`ThresholdCurve()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Instances`	`getCurve(FastVector predictions)` Calculates the performance stats for the default class and return results as a set of Instances.
`Instances`	`getCurve(FastVector predictions, int classIndex)` Calculates the performance stats for the desired class and return results as a set of Instances.
`static double`	`getNPointPrecision(Instances tcurve, int n)` Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
`String`	`getRevision()` Returns the revision string.
`static double`	`getROCArea(Instances tcurve)` Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
`static int`	`getThresholdInstance(Instances tcurve, double threshold)` Gets the index of the instance with the closest threshold value to the desired target
`static void`	`main(String[] args)` Tests the ThresholdCurve generation from the command line.

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

- Field Detail
  - RELATION_NAME
```
public static final String RELATION_NAME
```
    The name of the relation used in threshold curve datasets
    
    See Also:
    
    Constant Field Values
  - TRUE_POS_NAME
```
public static final String TRUE_POS_NAME
```
    attribute name: True Positives
    
    See Also:
    
    Constant Field Values
  - FALSE_NEG_NAME
```
public static final String FALSE_NEG_NAME
```
    attribute name: False Negatives
    
    See Also:
    
    Constant Field Values
  - FALSE_POS_NAME
```
public static final String FALSE_POS_NAME
```
    attribute name: False Positives
    
    See Also:
    
    Constant Field Values
  - TRUE_NEG_NAME
```
public static final String TRUE_NEG_NAME
```
    attribute name: True Negatives
    
    See Also:
    
    Constant Field Values
  - FP_RATE_NAME
```
public static final String FP_RATE_NAME
```
    attribute name: False Positive Rate"
    
    See Also:
    
    Constant Field Values
  - TP_RATE_NAME
```
public static final String TP_RATE_NAME
```
    attribute name: True Positive Rate
    
    See Also:
    
    Constant Field Values
  - PRECISION_NAME
```
public static final String PRECISION_NAME
```
    attribute name: Precision
    
    See Also:
    
    Constant Field Values
  - RECALL_NAME
```
public static final String RECALL_NAME
```
    attribute name: Recall
    
    See Also:
    
    Constant Field Values
  - FALLOUT_NAME
```
public static final String FALLOUT_NAME
```
    attribute name: Fallout
    
    See Also:
    
    Constant Field Values
  - FMEASURE_NAME
```
public static final String FMEASURE_NAME
```
    attribute name: FMeasure
    
    See Also:
    
    Constant Field Values
  - SAMPLE_SIZE_NAME
```
public static final String SAMPLE_SIZE_NAME
```
    attribute name: Sample Size
    
    See Also:
    
    Constant Field Values
  - LIFT_NAME
```
public static final String LIFT_NAME
```
    attribute name: Lift
    
    See Also:
    
    Constant Field Values
  - THRESHOLD_NAME
```
public static final String THRESHOLD_NAME
```
    attribute name: Threshold
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - ThresholdCurve
```
public ThresholdCurve()
```
- Method Detail
  - getCurve
```
public Instances getCurve(FastVector predictions)
```
    Calculates the performance stats for the default class and return results as a set of Instances. The structure of these Instances is as follows:
    - True Positives
    - False Negatives
    - False Positives
    - True Negatives
    - False Positive Rate
    - True Positive Rate
    - Precision
    - Recall
    - Fallout
    - Threshold contains the probability threshold that gives rise to the previous performance values.
    For the definitions of these measures, see TwoClassStats
    Parameters:
    
    predictions - the predictions to base the curve on
    
    Returns:
    
    datapoints as a set of instances, null if no predictions have been made.
    
    See Also:
    
    TwoClassStats
  - getCurve
```
public Instances getCurve(FastVector predictions,
                          int classIndex)
```
    Calculates the performance stats for the desired class and return results as a set of Instances.
    
    Parameters:
    
    predictions - the predictions to base the curve on
    
    classIndex - index of the class of interest.
    
    Returns:
    
    datapoints as a set of instances.
  - getNPointPrecision
```
public static double getNPointPrecision(Instances tcurve,
                                        int n)
```
    Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
    
    Parameters:
    
    tcurve - a previously extracted threshold curve Instances.
    
    n - the number of points to average over.
    
    Returns:
    
    the n-point precision.
  - getROCArea
```
public static double getROCArea(Instances tcurve)
```
    Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
    
    Parameters:
    
    tcurve - a previously extracted threshold curve Instances.
    
    Returns:
    
    the ROC area, or Double.NaN if you don't pass in a ThresholdCurve generated Instances.
  - getThresholdInstance
```
public static int getThresholdInstance(Instances tcurve,
                                       double threshold)
```
    Gets the index of the instance with the closest threshold value to the desired target
    
    Parameters:
    
    tcurve - a set of instances that have been generated by this class
    
    threshold - the target threshold
    
    Returns:
    
    the index of the instance that has threshold closest to the target, or -1 if this could not be found (i.e. no data, or bad threshold target)
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] args)
```
    Tests the ThresholdCurve generation from the command line. The classifier is currently hardcoded. Pipe in an arff file.
    
    Parameters:
    
    args - currently ignored

Class ThresholdCurve

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

RELATION_NAME

TRUE_POS_NAME

FALSE_NEG_NAME

FALSE_POS_NAME

TRUE_NEG_NAME

FP_RATE_NAME

TP_RATE_NAME

PRECISION_NAME

RECALL_NAME

FALLOUT_NAME

FMEASURE_NAME

SAMPLE_SIZE_NAME

LIFT_NAME

THRESHOLD_NAME

Constructor Detail

ThresholdCurve

Method Detail

getCurve

getCurve

getNPointPrecision

getROCArea

getThresholdInstance

getRevision

main