torchmetrics.retrieval.RetrievalPrecisionRecallCurve plots precision calls it False Positive Rate

🐛 Bug

RetrievalPrecisionRecallCurve plots precision (x axis) and recall (y axis). However, the plot calls them False and True positive rates. While True positive rate is the same as recall, false positive rate is not the same as precision.

1. why call it FPR/TPR instead of precision and recall which are the names used on the class?
2. is the FPR label on the x axis wrong or is the computation of precision a mistake and it was meant actually to compute FPR?

As a minor issue, usually, recall is on the x axis and precision on the y axis (just google precision recall curves), and would be nice if they were swapped.

To Reproduce

Precision is true_positives / (true_positives + false _positives), recall is true_positives / real_positives, and FPR is false_positives / total_population`. Given this data:

pred = torch.tensor([1.0, .9, .8, .7, .6, .5, .4, .3, .2, .1])
target = torch.tensor([1, 1, 0, 1, 1, 0, 0, 1, 0, 0], dtype=int)

We should expect:

yet, we get

>> metric = RetrievalPrecisionRecallCurve()
>> metric.update(pred, target,indexes=torch.zeros(pred.shape, dtype=int))
>> metric.compute()
(tensor([1.0000, 1.0000, 0.6667, 0.7500, 0.8000, 0.6667, 0.5714, 0.6250, 0.5556,
         0.5000]),
 tensor([0.2000, 0.4000, 0.4000, 0.6000, 0.8000, 0.8000, 0.8000, 1.0000, 1.0000,
         1.0000]),
 tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]))
>> fig, ax = metric.plot()
>> ax.set_xlim(0, 1)
>> ax.set_ylim(0, 1)

Start by reproducing the provided example with RetrievalPrecisionRecallCurve and inspect its plot() output, including the axis labels and orientation. Trace how the computed precision and recall values are passed to the plot, then verify the result against the definitions in the issue. Done means the plot labels and axes accurately describe the returned values, with tests covering the example.