*This tutorial illustrates the use of the functions
vl_roc,
vl_det, and
vl_pr to generate ROC, DET, and precision-recall
curves.*

VLFeat includes support for plotting starndard information
retrieval curves such as the *Receiver Operating Characteristic
(ROC)* and the *Precision-Recall (PR)* curves.

Consider a set of samples with labels `labels`

and
score `scores`

. `scores`

is typically the output
of a classifier, with higher scores corresponding to positive
labels. Ideally, sorting the data by decreasing scores should leave
all the positive samples first and the negative samples last. In
practice, a classifier is not perfect and the ranking is not ideal.
The tools discussed in this tutorial allow to evaluate and visualize
the quality of the ranking.

For the sake of the illustration generate some data randomly as follows:

```
numPos = 20 ;
numNeg = 100 ;
labels = [ones(1, numPos) -ones(1,numNeg)] ;
scores = randn(size(labels)) + labels ;
```

In this case, there have five times more negative samples than positive ones. The scores are correlated to the labels as expected, but do not allow for a perfect separation of the two classes.

# ROC and DET curves

To visualize the quality of the ranking, one can plot the ROC curve
by using the `vl_roc`

function:

```
vl_roc(labels, scores) ;
```

This produces the figure

The ROC curve is the parametric curve given by the true positve
rate (TPR) against the true negative rate (TNR). These two quantities
can be obtained from `vl_roc`

as follows:

```
[tpr, tnr] = vl_roc(labels, scores) ;
```

The TPR value `tpr(k)`

is the percentage of positive
samples that have rank smaller or equal than `k`

(where
ranks are assigned by decreasing scores). `tnr(k)`

is
instead the percentage of negative samples that have rank larger
than `k`

. Therefore, if one classifies the samples with
rank smaller or equal than `k`

to be positive and the rest
to be negative, `tpr(k)`

and `tnr(k)`

are
repsectively the probability that a positive/negative sample is
classified correctly.

Moving from rank `k`

to rank `k+1`

, if the
sample of rank `k+1`

is positive then `tpr`

increases; otherwise `tnr`

decreases. An ideal classifier
has all the positive samples first, and the corresponding ROC curve is
one that describes two sides of the unit square.

The *Area Under the Curve (AUC)* is an indicator of the
overall quality of a ROC curve. For example, the ROC of the ideal
classifier has AUC equal to 1. Another indicator is the *Equal
Error Rate (EER)*, the point on the ROC curve that corresponds to
have an equal probability of miss-classifying a positive or negative
sample. This point is obtained by intersecting the ROC curve with a
diagonal of the unit square. Both AUC and EER can be computed
by `vl_roc`

:

```
[tpr, tnr, info] = vl_roc(labels, scores) ;
disp(info.auc) ;
disp(info.eer) ;
```

`vl_roc`

has a couple of useful functionalities:

- Any sample with label equal to zero is effecitvely ignored in the evaluation.
- Samples with scores equal to
`-inf`

are assumed to be never retrieved by the classifier. For these, the TNR is conventionally set to be equal to zero. - Additional negative and positive samples with
`-inf`

score can be added to the evaluation by means of the`numNegatives`

and`numPositives`

options. For example,`vl_roc(labels,scores,'numNegatives',1e4)`

sets the number of negative samples to 10,000. This can be useful when evaluating large retrieval systems, for which one may want to record in`labels`

and`scores`

only the top ranked results from a classifier. - Different variants of the ROC plot can be produced. For example
`vl_roc(labels,scores,'plot','tptn')`

swaps the two axis, plotting the TNR against the TPR. Since the TPR is also the recall (i.e., the percentage of positive samples retrieved up to a certain rank), this makes the plot more directly comparable to a precision-recall plot.

A limitation of the ROC curves in evaluating a typical retrieval
system is that they put equal emphasis on false positive and false
negative errors. In a tipical retrieval application, however, the vast
majority of the samples are negative, so the false negative rate is
typically very small for any operating point of interest. Therefore
the emphasis is usually on the very first portion of the rank, where
the few positive samples should concentrate. This can be emphasized by
using either precision-recall plot or a
variant of the ROC curves called *Detection Error Tradeoff (DET)
curves*.

A DET curve plots the FNR (also called *false alarm rate*)
against teh FPR (also called *miss rate*) in logarithmic
coordiantes. It can be generated
by `vl_det`

function
call:

# Precision-recall curves

Both ROC and DET curves normalize out the relative proportions of
positive and negative samples. By contrast, a *Precision-Recall
(PR)* curve reflects this directly. One can plot the PR curve by
using the `vl_pr`

function:

```
vl_pr(labels, scores) ;
```

This produces the figure

The PR curve is the parametric curve given by precision and
recall. These two quantities can be obtained from `vl_roc`

as follows:

```
[recall, precision] = vl_roc(labels, scores) ;
```

The precision value `precision(k)`

is the proportion of
samples with rank smaller or equal than `k-1`

that are
positive(where ranks are assigned by decreasing
scores). `recall(k)`

is instead the percentage of positive
samples that have rank smaller or equal than `k-1`

. For
example, if the first two samples are one positive and one
negative, `precision(3)`

is 1/2. If there are in total 5
positive samples, then `recall(3)`

is 1/5.

Moving from rank `k`

to rank `k+1`

, if the
sample of rank `k+1`

is positive then
both `precision`

and `recall`

increase;
otherwise `precision`

decreases and `recall`

stays constant. This gives the PR curve a characteristic
saw-shape. For aan ideal classifier that ranks all the positive
samples first the PR curve is one that describes two sides of the unit
square.

Similar to the ROC curves, the *Area Under the Curve (AUC)*
can be used to summarize the quality of a ranking in term of precision
and recall. This can be obtained as `info.auc`

by

```
[rc, pr, info] = vl_pr(labels, scores) ;
disp(info.auc) ;
disp(info.ap) ;
disp(info.ap_interp_11) ;
```

The AUC is obtained by trapezoidal interpolation of the precision.
An alternative and usually almost equivalent metric is the *Average
Precision (AP)*, returned as `info.ap`

. This is the
average of the precision obtained every time a new positive sample is
recalled. It is the same as the AUC if precision is interpolated by
constant segments and is the definition used by TREC most
often. Finally, the *11 points interpolated average precision*,
returned as `info.ap_interp_11`

. This is an older TREC
definition and is obtained by taking the average of eleven precision
values, obtained as the maximum precision for recalls largerer than
0.0, 0.1, ..., 1.0. This particular metric was used, for example, in
the PASCAL VOC challenge until the 2008 edition.