mioXpektron.analysis.metrics

Classification metrics and diagnostic plots.

Functions

calculate_multiclass_metrics(y_true, y_pred, *)	Compute overall and per-class classification metrics.
get_class_names(*[, data_dict, ...])	Resolve human-readable class names for encoded labels.
plot_confusion_matrix(y_true, y_pred, ...[, ...])	Plot and save a confusion matrix heatmap.

mioXpektron.analysis.metrics.get_class_names(*, data_dict=None, label_encoder=None, class_names=None, n_classes=None)

Resolve human-readable class names for encoded labels.

Parameters:

• data_dict (Mapping[str, Any] | None)

• label_encoder (Any | None)

• class_names (Sequence[str] | None)

• n_classes (int | None)

Return type:

List[str]

mioXpektron.analysis.metrics.calculate_multiclass_metrics(y_true, y_pred, *, y_proba=None, class_names=None, data_dict=None)

Compute overall and per-class classification metrics.

Parameters:

• y_true (ndarray)

• y_pred (ndarray)

• y_proba (ndarray | None)

• class_names (Sequence[str] | None)

• data_dict (Mapping[str, Any] | None)

Return type:

Dict[str, Any]

mioXpektron.analysis.metrics.plot_confusion_matrix(y_true, y_pred, savepath, *, class_names=None, data_dict=None, normalize=False, title='Confusion matrix')

Plot and save a confusion matrix heatmap.

Parameters:

• y_true (ndarray)

• y_pred (ndarray)

• savepath (str)

• class_names (Sequence[str] | None)

• data_dict (Mapping[str, Any] | None)

• normalize (bool)

• title (str)

Return type:

ndarray