torch_ecg.utils.metrics_from_confusion_matrix

torch_ecg.utils.metrics_from_confusion_matrix(labels: Union[numpy.ndarray, torch.Tensor], outputs: Union[numpy.ndarray, torch.Tensor], num_classes: Optional[int] = None, weights: Optional[Union[numpy.ndarray, torch.Tensor]] = None, thr: float = 0.5) → Dict[str, Union[float, numpy.ndarray]][source]

Compute macro metrics, and metrics for each class.

Parameters

  • labels (numpy.ndarray or torch.Tensor) – Binary labels, of shape (n_samples, n_classes), or indices of each label class, of shape (n_samples,).

  • outputs (numpy.ndarray or torch.Tensor) – Probability outputs, of shape (n_samples, n_classes), or binary outputs, of shape (n_samples, n_classes), or indices of each class predicted, of shape (n_samples,).

  • num_classes (int, optional) – Number of classes. If labels and outputs are both of shape (n_samples,), then num_classes must be specified.

  • weights (numpy.ndarray or torch.Tensor, optional) – Weights for each class, of shape (n_classes,), used to compute macro metrics.

  • thr (float, default: 0.5) – Threshold for binary classification, valid only if outputs is of shape (n_samples, n_classes).

Returns

metrics – Metrics computed from the one-vs-rest confusion matrix.

Return type

dict

Examples

>>> from torch_ecg.cfg import DEFAULTS
>>> # binary labels (100 samples, 10 classes, multi-label)
>>> labels = DEFAULTS.RNG_randint(0, 1, (100, 10))
>>> # probability outputs (100 samples, 10 classes, multi-label)
>>> outputs = DEFAULTS.RNG.random((100, 10))
>>> metrics = metrics_from_confusion_matrix(labels, outputs)
>>> # binarize outputs (100 samples, 10 classes, multi-label)
>>> outputs = DEFAULTS.RNG_randint(0, 1, (100, 10))
>>> # would raise
>>> # RuntimeWarning: `outputs` is probably binary, AUC may be incorrect
>>> metrics = metrics_from_confusion_matrix(labels, outputs)
>>> # categorical outputs (100 samples, 10 classes)
>>> outputs = DEFAULTS.RNG_randint(0, 9, (100,))
>>> # would raise
>>> # RuntimeWarning: `outputs` is probably binary, AUC may be incorrect
>>> metrics = metrics_from_confusion_matrix(labels, outputs)