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## weka.classifiers.evaluation
Class ThresholdCurve

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

All Implemented Interfaces:
  RevisionHandler

    public class ThresholdCurve

extends java.lang.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: 5987 $
Author:
  Len Trigg (len@reeltwo.com)

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

Constructor Summary
`ThresholdCurve()`

Method Summary
`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.
`java.lang.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(java.lang.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 java.lang.String RELATION_NAME

  The name of the relation used in threshold curve datasets

  See Also:
    Constant Field Values

### TRUE_POS_NAME

    public static final java.lang.String TRUE_POS_NAME

  attribute name: True Positives

  See Also:
    Constant Field Values

### FALSE_NEG_NAME

    public static final java.lang.String FALSE_NEG_NAME

  attribute name: False Negatives

  See Also:
    Constant Field Values

### FALSE_POS_NAME

    public static final java.lang.String FALSE_POS_NAME

  attribute name: False Positives

  See Also:
    Constant Field Values

### TRUE_NEG_NAME

    public static final java.lang.String TRUE_NEG_NAME

  attribute name: True Negatives

  See Also:
    Constant Field Values

### FP_RATE_NAME

    public static final java.lang.String FP_RATE_NAME

  attribute name: False Positive Rate"

  See Also:
    Constant Field Values

### TP_RATE_NAME

    public static final java.lang.String TP_RATE_NAME

  attribute name: True Positive Rate

  See Also:
    Constant Field Values

### PRECISION_NAME

    public static final java.lang.String PRECISION_NAME

  attribute name: Precision

  See Also:
    Constant Field Values

### RECALL_NAME

    public static final java.lang.String RECALL_NAME

  attribute name: Recall

  See Also:
    Constant Field Values

### FALLOUT_NAME

    public static final java.lang.String FALLOUT_NAME

  attribute name: Fallout

  See Also:
    Constant Field Values

### FMEASURE_NAME

    public static final java.lang.String FMEASURE_NAME

  attribute name: FMeasure

  See Also:
    Constant Field Values

### SAMPLE_SIZE_NAME

    public static final java.lang.String SAMPLE_SIZE_NAME

  attribute name: Sample Size

  See Also:
    Constant Field Values

### LIFT_NAME

    public static final java.lang.String LIFT_NAME

  attribute name: Lift

  See Also:
    Constant Field Values

### THRESHOLD_NAME

    public static final java.lang.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 java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] args)

  Tests the ThresholdCurve generation from the command line. The classifier is currently hardcoded. Pipe in an arff file.

  Parameters:
    `args` - currently ignored

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