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"]