pyhealth.metrics.multiclass#

pyhealth.metrics.multiclass.multiclass_metrics_fn(y_true, y_prob, metrics=None)[source]#

Computes metrics for multiclass classification.

User can specify which metrics to compute by passing a list of metric names. The accepted metric names are:

  • roc_auc_macro_ovo: area under the receiver operating characteristic curve,

    macro averaged over one-vs-one multiclass classification

  • roc_auc_macro_ovr: area under the receiver operating characteristic curve,

    macro averaged over one-vs-rest multiclass classification

  • roc_auc_weighted_ovo: area under the receiver operating characteristic curve,

    weighted averaged over one-vs-one multiclass classification

  • roc_auc_weighted_ovr: area under the receiver operating characteristic curve,

    weighted averaged over one-vs-rest multiclass classification

  • accuracy: accuracy score

  • balanced_accuracy: balanced accuracy score (usually used for imbalanced

    datasets)

  • f1_micro: f1 score, micro averaged

  • f1_macro: f1 score, macro averaged

  • f1_weighted: f1 score, weighted averaged

  • jaccard_micro: Jaccard similarity coefficient score, micro averaged

  • jaccard_macro: Jaccard similarity coefficient score, macro averaged

  • jaccard_weighted: Jaccard similarity coefficient score, weighted averaged

  • cohen_kappa: Cohen’s kappa score

If no metrics are specified, accuracy, f1_macro, and f1_micro are computed by default.

This function calls sklearn.metrics functions to compute the metrics. For more information on the metrics, please refer to the documentation of the corresponding sklearn.metrics functions.

Parameters

  • y_true (ndarray) – True target values of shape (n_samples,).

  • y_prob (ndarray) – Predicted probabilities of shape (n_samples, n_classes).

  • metrics (Optional[List[str]]) – List of metrics to compute. Default is [“accuracy”, “f1_macro”, “f1_micro”].

Return type

Dict[str, float]

Returns

Dictionary of metrics whose keys are the metric names and values are

the metric values.

Examples

>>> from pyhealth.metrics import multiclass_metrics_fn
>>> y_true = np.array([0, 1, 2, 2])
>>> y_prob = np.array([[0.9,  0.05, 0.05],
...                    [0.05, 0.9,  0.05],
...                    [0.05, 0.05, 0.9],
...                    [0.6,  0.2,  0.2]])
>>> multiclass_metrics_fn(y_true, y_prob, metrics=["accuracy"])
{'accuracy': 0.75}