torch_ecg.utils.metrics_from_confusion_matrix

torch_ecg.utils.metrics_from_confusion_matrix(labels: ndarray | Tensor, outputs: ndarray | Tensor, num_classes: int | None = None, weights: ndarray | Tensor | None = None, thr: float = 0.5, fillna: bool | float = 0.0) → Dict[str, float | 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).

  • fillna (bool or float, default: 0.0) – If is False, then NaN will be left in the result. If is True, then NaN will be filled with 0.0. If is a float, then NaN will be filled with the specified value.

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)