extract_pos_proba

skfp.metrics.extract_pos_proba(predictions: ndarray | list[ndarray]) → ndarray

Extract positive class probabilities (y-score).

Probabilitic metrics like AUROC or AUPRC use predicted probabilities. This function extracts them from .predict_proba() results.

Returns (n_samples,) shape for single-task, and (n_samples, n_tasks) for multioutput problems.

Parameters:

predictions (list of NumPy arrays) – Raw predictions of shape (n_samples,2) (single-task) or (n_tasks, n_samples, 2) (multioutput), with predicted negative and positive class probability in the last dimension.

Returns:

y_score – Predicted positive class probabilities for each task.

Return type:

NumPy array of shape (n_samples,) or (n_samples, n_tasks

Examples

>>> import numpy as np
>>> from skfp.metrics import extract_pos_proba
>>> y_pred = np.array([[0.6, 0.1], [0.2, 0.3]])
>>> extract_pos_proba(y_pred)
array([0.1, 0.3])
>>> y_pred = [np.array([[0.6, 0.1], [0.2, 0.3]]), np.array([[0.0, 0.9], [0.7, 0.8]])]
>>> extract_pos_proba(y_pred)
array([[0.1, 0.9],
       [0.3, 0.8]])