def value()

in torchnet/meter/apmeter.py [0:0]

• 27 lines of code
• 8 McCabe index (conditional complexity)

    def value(self):
        """Returns the model's average precision for each class

        Return:
            ap (FloatTensor): 1xK tensor, with avg precision for each class k

        """

        if self.scores.numel() == 0:
            return 0
        ap = torch.zeros(self.scores.size(1))
        if hasattr(torch, "arange"):
            rg = torch.arange(1, self.scores.size(0) + 1).float()
        else:
            rg = torch.range(1, self.scores.size(0)).float()
        if self.weights.numel() > 0:
            weight = self.weights.new(self.weights.size())
            weighted_truth = self.weights.new(self.weights.size())

        # compute average precision for each class
        for k in range(self.scores.size(1)):
            # sort scores
            scores = self.scores[:, k]
            targets = self.targets[:, k]
            _, sortind = torch.sort(scores, 0, True)
            truth = targets[sortind]
            if self.weights.numel() > 0:
                weight = self.weights[sortind]
                weighted_truth = truth.float() * weight
                rg = weight.cumsum(0)

            # compute true positive sums
            if self.weights.numel() > 0:
                tp = weighted_truth.cumsum(0)
            else:
                tp = truth.float().cumsum(0)

            # compute precision curve
            precision = tp.div(rg)

            # compute average precision
            ap[k] = precision[truth.bool()].sum() / max(float(truth.sum()), 1)
        return ap