Documentation>MATLAB API>PLOTOP - vl_roc

[TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating Characteristic (ROC) curve [1]. LABELS is a row vector of ground truth labels, greater than zero for a positive sample and smaller than zero for a negative one. SCORES is a row vector of corresponding sample scores, usually obtained from a classifier. The scores induce a ranking of the samples where larger scores should correspond to positive labels.

Without output arguments, the function plots the ROC graph of the specified data in the current graphical axis.

Otherwise, the function returns the true positive and true negative rates TPR and TNR. These are vectors of the same size of LABELS and SCORES and are computed as follows. Samples are ranked by decreasing scores, starting from rank 1. TPR(K) and TNR(K) are the true positive and true negative rates when samples of rank smaller or equal to K-1 are predicted to be positive. So for example TPR(3) is the true positive rate when the two samples with largest score are predicted to be positive. Similarly, TPR(1) is the true positive rate when no samples are predicted to be positive, i.e. the constant 0.

Setting a label to zero ignores the corresponding sample in the calculations, as if the sample was removed from the data. Setting the score of a sample to -INF causes the function to assume that that sample was never retrieved. If there are samples with -INF score, the ROC curve is incomplete as the maximum recall is less than 1.

[TPR,TNR,INFO] = VL_ROC(...) returns an additional structure INFO with the following fields:

info.auc Area under the ROC curve (AUC).

This is the area under the ROC plot, the parametric curve (FPR(S), TPR(S)). The PLOT option can be used to plot variants of this curve, which affects the calculation of a corresponding AUC.

info.eer Equal error rate (EER).

The equal error rate is the value of FPR (or FNR) when the ROC curves intersects the line connecting (0,0) to (1,1).

info.eerThreshold EER threshold.

The value of the score for which the EER is attained.

VL_ROC() accepts the following options:

Plot []

Setting this option turns on plotting unconditionally. The following plot variants are supported:

tntp Plot TPR against TNR (standard ROC plot).
tptn Plot TNR against TPR (recall on the horizontal axis).
fptp Plot TPR against FPR.
fpfn Plot FNR against FPR (similar to a DET curve).

Note that this option will affect the INFO.AUC value computation too.

NumPositives []
NumNegatives []

If either of these parameters is set to a number, the function pretends that LABELS contains the specified number of positive/negative labels. NUMPOSITIVES/NUMNEGATIVES cannot be smaller than the actual number of positive/negative entries in LABELS. The additional positive/negative labels are appended to the end of the sequence as if they had -INF scores (as explained above, the function interprets such samples as `not retrieved'). This feature can be used to evaluate the performance of a large-scale retrieval experiment in which only a subset of highly-scoring results are recorded for efficiency reason.

Stable false

If set to true, TPR and TNR are returned in the same order of LABELS and SCORES rather than being sorted by decreasing score.

About the ROC curve

Consider a classifier that predicts as positive all samples whose score is not smaller than a threshold S. The ROC curve represents the performance of such classifier as the threshold S is changed. Formally, define

  P = overall num. of positive samples,
  N = overall num. of negative samples,

and for each threshold S

  TP(S) = num. of samples that are correctly classified as positive,
  TN(S) = num. of samples that are correctly classified as negative,
  FP(S) = num. of samples that are incorrectly classified as positive,
  FN(S) = num. of samples that are incorrectly classified as negative.

Consider also the rates:

  TPR = TP(S) / P,      FNR = FN(S) / P,
  TNR = TN(S) / N,      FPR = FP(S) / N,

and notice that, by definition,

  P = TP(S) + FN(S) ,    N = TN(S) + FP(S),
  1 = TPR(S) + FNR(S),   1 = TNR(S) + FPR(S).

The ROC curve is the parametric curve (FPR(S), TPR(S)) obtained as the classifier threshold S is varied in the reals. The TPR is the same as `recall' in a PR curve (see VL_PR()).

The ROC curve is contained in the square with vertices (0,0) The (average) ROC curve of a random classifier is a line which connects (1,0) and (0,1).

The ROC curve is independent of the prior probability of the labels (i.e. of P/(P+N) and N/(P+N)).


See also: VL_PR(), VL_DET(), VL_HELP().