mindspore.train.auc

mindspore.train.auc(x, y, reorder=False)[source]

Computes the AUC(Area Under the Curve) using the trapezoidal rule. This is a general function, given points on a curve, for computing the area under the ROC-curve.

Parameters

x (Union[np.array, list]) – From the ROC curve(fpr), np.array with false positive rates. If multiclass, this is a list of such np.array, one for each class. The shape \((N)\).
y (Union[np.array, list]) – From the ROC curve(tpr), np.array with true positive rates. If multiclass, this is a list of such np.array, one for each class. The shape \((N)\).
reorder (bool) – If False, x must rise or fall monotonously. If True, x will be sorted in ascending order. Default: False .

Returns

float, the area under the ROC-curve.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore.train import ROC, auc
>>>
>>> y_pred = np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]])
>>> y = np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]])
>>> metric = ROC(pos_label=2)
>>> metric.clear()
>>> metric.update(y_pred, y)
>>> fpr, tpr, thre = metric.eval()
>>> output = auc(fpr, tpr)
>>> print(output)
0.5357142857142857