# mars.learn.metrics.auc¶

mars.learn.metrics.auc(x, y, session=None, run_kwargs=None)[source]

Compute Area Under the Curve (AUC) using the trapezoidal rule

This is a general function, given points on a curve. For computing the area under the ROC-curve, see roc_auc_score(). For an alternative way to summarize a precision-recall curve, see average_precision_score().

Parameters
• x (tensor, shape = [n]) – x coordinates. These must be either monotonic increasing or monotonic decreasing.

• y (tensor, shape = [n]) – y coordinates.

Returns

auc

Return type

tensor, with float value

Examples

>>> import mars.tensor as mt
>>> from mars.learn import metrics
>>> y = mt.array([1, 1, 2, 2])
>>> pred = mt.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
>>> metrics.auc(fpr, tpr)
0.75


roc_auc_score
average_precision_score
precision_recall_curve