ClassificationMetrics

class torch_ecg.components.ClassificationMetrics(multi_label: bool = True, macro: bool = True, extra_metrics: Callable | None = None)

Bases: Metrics

Metrics for the task of classification.

Parameters:

• multi_label (bool, default True) – Whether is multi-label classification task.

• macro (bool, default True) – Whether to use macro-averaged metrics.

• extra_metrics (callable, optional) –

  Extra metrics to compute, has to be a function with signature:

  def extra_metrics(
      labels : np.ndarray
      outputs : np.ndarray
      num_classes : Optional[int]=None
      weights : Optional[np.ndarray]=None
  ) -> dict
  

compute(labels: ndarray | Tensor, outputs: ndarray | Tensor, num_classes: int | None = None, weights: ndarray | None = None, thr: float = 0.5) → ClassificationMetrics

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:

self – The metrics object itself with the computed metrics.

Return type:

ClassificationMetrics,

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 = ClassificationMetrics()
>>> metrics = metrics.compute(labels, outputs)
>>> metrics.fl_measure
0.5062821146226457
>>> metrics.set_macro(False)
>>> metrics.fl_measure
array([0.46938776, 0.4742268 , 0.4375    , 0.52941176, 0.58      ,
   0.57692308, 0.55769231, 0.48351648, 0.55855856, 0.3956044 ])
>>> # 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 = ClassificationMetrics()
>>> metrics = metrics.compute(labels, outputs)
>>> metrics.fl_measure
0.5062821146226457
>>> metrics.set_macro(False)
>>> metrics.fl_measure
array([0.46938776, 0.4742268 , 0.4375    , 0.52941176, 0.58      ,
   0.57692308, 0.55769231, 0.48351648, 0.55855856, 0.3956044 ])
>>> # 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 = ClassificationMetrics()
>>> metrics = metrics.compute(labels, outputs)
>>> metrics.fl_measure
0.5062821146226457
>>> metrics.set_macro(False)
>>> metrics.fl_measure
array([0.46938776, 0.4742268 , 0.4375    , 0.52941176, 0.58      ,
   0.57692308, 0.55769231, 0.48351648, 0.55855856, 0.3956044 ])

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().

set_macro(macro: bool) → None

Set whether to use macro-averaged metrics.

Parameters:

macro (bool) – Whether to use macro-averaged metrics.