def update()

in ignite/metrics/precision.py [0:0]

• 40 lines of code
• 9 McCabe index (conditional complexity)

    def update(self, output: Sequence[torch.Tensor]) -> None:
        self._check_shape(output)
        self._check_type(output)
        y_pred, y = output[0].detach(), output[1].detach()

        if self._type == "binary":
            y_pred = y_pred.view(-1)
            y = y.view(-1)
        elif self._type == "multiclass":
            num_classes = y_pred.size(1)
            if y.max() + 1 > num_classes:
                raise ValueError(
                    f"y_pred contains less classes than y. Number of predicted classes is {num_classes}"
                    f" and element in y has invalid class = {y.max().item() + 1}."
                )
            y = to_onehot(y.view(-1), num_classes=num_classes)
            indices = torch.argmax(y_pred, dim=1).view(-1)
            y_pred = to_onehot(indices, num_classes=num_classes)
        elif self._type == "multilabel":
            # if y, y_pred shape is (N, C, ...) -> (C, N x ...)
            num_classes = y_pred.size(1)
            y_pred = torch.transpose(y_pred, 1, 0).reshape(num_classes, -1)
            y = torch.transpose(y, 1, 0).reshape(num_classes, -1)

        # Convert from int cuda/cpu to double on self._device
        y_pred = y_pred.to(dtype=torch.float64, device=self._device)
        y = y.to(dtype=torch.float64, device=self._device)
        correct = y * y_pred
        all_positives = y_pred.sum(dim=0)

        if correct.sum() == 0:
            true_positives = torch.zeros_like(all_positives)
        else:
            true_positives = correct.sum(dim=0)

        if self._type == "multilabel":
            if not self._average:
                self._true_positives = torch.cat([self._true_positives, true_positives], dim=0)  # type: torch.Tensor
                self._positives = torch.cat([self._positives, all_positives], dim=0)  # type: torch.Tensor
            else:
                self._true_positives += torch.sum(true_positives / (all_positives + self.eps))
                self._positives += len(all_positives)
        else:
            self._true_positives += true_positives
            self._positives += all_positives

        self._updated = True