pyhealth.metrics.binary#

pyhealth.metrics.binary.binary_metrics_fn(y_true, y_prob, metrics=None, threshold=0.5)[source]#

Computes metrics for binary classification.

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

  • pr_auc: area under the precision-recall curve

  • roc_auc: area under the receiver operating characteristic curve

  • accuracy: accuracy score

  • balanced_accuracy: balanced accuracy score (usually used for imbalanced datasets)

  • f1: f1 score

  • precision: precision score

  • recall: recall score

  • cohen_kappa: Cohen’s kappa score

  • jaccard: Jaccard similarity coefficient score

  • ECE: Expected Calibration Error (with 20 equal-width bins). Check pyhealth.metrics.calibration.ece_confidence_binary().

  • ECE_adapt: adaptive ECE (with 20 equal-size bins). Check pyhealth.metrics.calibration.ece_confidence_binary().

If no metrics are specified, pr_auc, roc_auc and f1 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,).

  • metrics (Optional[List[str]]) – List of metrics to compute. Default is [“pr_auc”, “roc_auc”, “f1”].

  • threshold (float) – Threshold for binary classification. Default is 0.5.

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 binary_metrics_fn
>>> y_true = np.array([0, 0, 1, 1])
>>> y_prob = np.array([0.1, 0.4, 0.35, 0.8])
>>> binary_metrics_fn(y_true, y_prob, metrics=["accuracy"])
{'accuracy': 0.75}