# Source code for catalyst.metrics.precision

from typing import Optional
import torch
from catalyst.metrics.functional import preprocess_multi_label_metrics
[docs]def average_precision(
outputs: torch.Tensor,
targets: torch.Tensor,
weights: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Computes the average precision.
Args:
outputs: NxK tensor that for each of the N examples
indicates the probability of the example belonging to each of
the K classes, according to the model.
targets: binary NxK tensort that encodes which of the K
classes are associated with the N-th input
(eg: a row [0, 1, 0, 1] indicates that the example is
associated with classes 2 and 4)
weights: importance for each sample
Returns:
torch.Tensor: tensor of [K; ] shape,
with average precision for K classes
"""
# outputs - [bs; num_classes] with scores
# targets - [bs; num_classes] with binary labels
outputs, targets, weights = preprocess_multi_label_metrics(
outputs=outputs, targets=targets, weights=weights,
)
if outputs.numel() == 0:
return torch.zeros(1)
ap = torch.zeros(targets.size(1))
# compute average precision for each class
for class_i in range(targets.size(1)):
# sort scores
class_scores = outputs[:, class_i]
class_targets = targets[:, class_i]
_, sortind = torch.sort(class_scores, dim=0, descending=True)
correct = class_targets[sortind]
# compute true positive sums
if weights is not None:
class_weight = weights[sortind]
weighted_correct = correct.float() * class_weight
tp = weighted_correct.cumsum(0)
rg = class_weight.cumsum(0)
else:
tp = correct.float().cumsum(0)
rg = torch.arange(1, targets.size(0) + 1).float()
# compute precision curve
precision = tp.div(rg)
# compute average precision
ap[class_i] = precision[correct.bool()].sum() / max(
float(correct.sum()), 1
)
return ap
__all__ = ["average_precision"]