为什么 roc_curve 只返回 3 个值? [英] Why does roc_curve return only 3 values?

问题描述

我将标签为 0 和 1 的形状 (999,) 的 y_true 和 y_pred 传递给 FP, TP, threshold =roc_curve(y_true, y_pred, pos_label=1)结果 array 只有 3 个元素被返回.有什么问题?

I pass y_true and y_pred of shapes (999,) with labels 0 and 1 to FP, TP, threshold = roc_curve(y_true, y_pred, pos_label=1) and as a result array of only 3 elements is being returned. What can be wrong?

整个代码片段

def get_roc_auc_scores(y_pred_labels, y_true_labels):
roc_auc_scores = {key: {'FP': [], 'TP': [], 'Scores': []} for key in ['Low', 'High']}
for key in roc_auc_scores.keys():
    for y_pred in y_pred_labels:
        # Get True Positive and False Positive labels
        fp, tp, thresh = roc_curve(y_true_labels[key], y_pred, pos_label=1)
        roc_auc_scores[key]['FP'].append(fp)
        roc_auc_scores[key]['TP'].append(tp)
        
        # Get AUC score
        auc_score = roc_auc_score(y_true_labels[key], y_pred)
        roc_auc_scores[key]['Scores'].append(auc_score)
return roc_auc_scores

其中 y_pred_labels 是一个包含 6 个带有预测的 arrays 的列表，y_true_labels 包含真实的标签.高和低仅描述特定情况.

where y_pred_labels is a list containing 6 arrays with predictions and y_true_labels contains true labels. High and Low describe only the specific case.

推荐答案

ROC 曲线是通过改变决策阈值来构建的:您不应将硬类预测作为 y_score 传递，而是通过概率分数或其他一些置信度量.

ROC curves are built by varying a decision threshold: you should not pass hard class predictions as y_score, but instead the probability scores or some other confidence measure.

来自文档:

y_score : array, shape = [n_samples]

目标分数，可以是正类的概率估计、置信度值或决策的非阈值度量(如某些分类器上的decision_function"返回).

Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers).

通过硬二元分类，只有三个相关阈值:一个低于 0，一个介于 0 和 1 之间，一个大于 1.

Passing a hard binary classification, there are only three relevant thresholds: one below 0, one between 0 and 1, and one greater than 1.

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