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## Possible error in the `ap_per_class` function when calculating the precision-recall curve
Hello,
I have identified a possible error in the `ap_per_class` function when calculating the metrics for the precision-recall curve. Below, I provide an example to explain it more clearly:
Let’s assume the `unique_classes` list is `[0, 1, 2]`, and the model never predicted class 1. In this case, on line 583 of the `utils/metrics.py` file, the loop will skip to the next class (class 2) because `n_p == 0` for class 1.
However, before moving on to the next class, a zero-filled array corresponding to class 1 should be added to the `prec_values` list. Otherwise, the precision-recall curve for class 2 will incorrectly appear as if it belongs to class 1, and the legend will also fail to show the correct label for class 2.
To fix this behavior, before the `continue` statement, the code should be modified as follows:
```python
if n_p == 0 or n_l == 0:
prec_values.append(np.zeros(1000))
continue
```
I hope my explanation is clear, and I have correctly identified the issue. While this is a rare case where the model never predicts one of the classes in the dataset, it is still something to consider to avoid errors in the precision-recall curve visualizations.
Best regards.