def compute(
self
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor],
List[Tensor]]]:
"""
Compute the precision-recall curve
Returns:
3-element tuple containing
precision:
tensor where element i is the precision of predictions with
score >= thresholds[i] and the last element is 1.
If multiclass, this is a list of such tensors, one for each class.
recall:
tensor where element i is the recall of predictions with
score >= thresholds[i] and the last element is 0.
If multiclass, this is a list of such tensors, one for each class.
thresholds:
Thresholds used for computing precision/recall scores
"""
preds = torch.cat(self.preds, dim=0)
target = torch.cat(self.target, dim=0)
return _precision_recall_curve_compute(preds, target, self.num_classes,
self.pos_label)
def compute(
self
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor],
List[Tensor]]]:
"""Compute the precision-recall curve.
Returns:
3-element tuple containing
precision:
tensor where element ``i`` is the precision of predictions with
``score >= thresholds[i]`` and the last element is 1.
If multiclass, this is a list of such tensors, one for each class.
recall:
tensor where element ``i`` is the recall of predictions with
``score >= thresholds[i]`` and the last element is 0.
If multiclass, this is a list of such tensors, one for each class.
thresholds:
Thresholds used for computing precision/recall scores
"""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
if not self.num_classes:
raise ValueError(
f"`num_classes` bas to be positive number, but got {self.num_classes}"
)
return _precision_recall_curve_compute(preds, target, self.num_classes,
self.pos_label)
def _average_precision_compute(
preds: Tensor,
target: Tensor,
num_classes: int,
pos_label: Optional[int] = None,
average: Optional[str] = "macro",
sample_weights: Optional[Sequence] = None,
) -> Union[List[Tensor], Tensor]:
"""Computes the average precision score.
Args:
preds: predictions from model (logits or probabilities)
target: ground truth values
num_classes: integer with number of classes.
pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translated
to 1. For multiclass problems his argument should not be set as we iteratively change it in the
range ``[0, num_classes-1]``
average: reduction method for multi-class or multi-label problems
sample_weights: sample weights for each data point
Example:
>>> # binary case
>>> preds = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> pos_label = 1
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, pos_label=pos_label)
>>> _average_precision_compute(preds, target, num_classes, pos_label)
tensor(1.)
>>> # multiclass case
>>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
... [0.05, 0.75, 0.05, 0.05, 0.05],
... [0.05, 0.05, 0.75, 0.05, 0.05],
... [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> num_classes = 5
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, num_classes)
>>> _average_precision_compute(preds, target, num_classes, average=None)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
"""
# todo: `sample_weights` is unused
precision, recall, _ = _precision_recall_curve_compute(
preds, target, num_classes, pos_label)
if average == "weighted":
if preds.ndim == target.ndim and target.ndim > 1:
weights = target.sum(dim=0).float()
else:
weights = _bincount(target, minlength=num_classes).float()
weights = weights / torch.sum(weights)
else:
weights = None
return _average_precision_compute_with_precision_recall(
precision, recall, num_classes, average, weights)
def _average_precision_compute(
preds: Tensor,
target: Tensor,
num_classes: int,
pos_label: int,
sample_weights: Optional[Sequence] = None,
) -> Union[List[Tensor], Tensor]:
# todo: `sample_weights` is unused
precision, recall, _ = _precision_recall_curve_compute(
preds, target, num_classes, pos_label)
return _average_precision_compute_with_precision_recall(
precision, recall, num_classes)
def _average_precision_compute(
preds: Tensor,
target: Tensor,
num_classes: int,
pos_label: int,
sample_weights: Optional[Sequence] = None,
) -> Union[List[Tensor], Tensor]:
# todo: `sample_weights` is unused
precision, recall, _ = _precision_recall_curve_compute(preds, target, num_classes, pos_label)
# Return the step function integral
# The following works because the last entry of precision is
# guaranteed to be 1, as returned by precision_recall_curve
if num_classes == 1:
return -torch.sum((recall[1:] - recall[:-1]) * precision[:-1])
res = []
for p, r in zip(precision, recall):
res.append(-torch.sum((r[1:] - r[:-1]) * p[:-1]))
return res
