我想以precision_recall_curve提供精确度和召回率的方式获取特异度。

from sklearn.metrics import precision_recall_curveprecisions, recalls, thresholds = precision_recall_curve(    ground_truth,    predictions,)

我该如何实现这一点？

回答：

因此，我查看了sklearn.metrics.precision_recall_curve的源代码（https://github.com/scikit-learn/scikit-learn/blob/2e90b897768fd360ef855cb46e0b37f2b6faaf72/sklearn/metrics/_ranking.py），并根据我的需求进行了修改。

import numpy as npfrom sklearn.metrics.ranking import _binary_clf_curvedef specificity_sensitivity_curve(y_true, probas_pred):    """    计算不同概率阈值下的特异度-敏感度对。    作为参考，请参见'precision_recall_curve'    """    fps, tps, thresholds = _binary_clf_curve(y_true, probas_pred)    sensitivity = tps / tps[-1]    specificity = (fps[-1] - fps) / fps[-1]    last_ind = tps.searchsorted(tps[-1])    sl = slice(last_ind, None, -1)    return np.r_[specificity[sl], 1], np.r_[sensitivity[sl], 0], thresholds[sl]