AUC 曲线甚至没有显示在图上

Question

使用逻辑回归进行预测后，得到的混淆矩阵如下：

True Positives: 3
False Positives: 1309
True Negatives: 12361
False Negatives: 4

roc_auc_score 在这里：

roc_auc_score(y_test, log_preds)
0.6664071480823492

所以我想用这段代码来可视化它：

probas = lg.predict_proba(X_test)[:, 1]
def get_preds(threshold, probabilities):
    return [1 if prob > threshold else 0 for prob in probabilities]
roc_values = []
for thresh in np.linspace(0, 1, 100):
    preds = get_preds(thresh, probas)
    tn, fp, fn, tp = confusion_matrix(y_test, log_preds).ravel()
    tpr = tp/(tp+fn)
    fpr = fp/(fp+tn)
    roc_values.append([tpr, fpr])
tpr_values, fpr_values = zip(*roc_values)
fig, ax = plt.subplots(figsize=(10,7))
ax.plot(fpr_values, tpr_values)
ax.plot(np.linspace(0, 1, 100),
         np.linspace(0, 1, 100),
         label='baseline',
         linestyle='--')
plt.title('Receiver Operating Characteristic Curve', fontsize=18)
plt.ylabel('TPR', fontsize=16)
plt.xlabel('FPR', fontsize=16)
plt.legend(fontsize=12);

下面是输出，它只有一个基线，我不明白。 （我的声誉还不足以嵌入图片，请随时编辑。谢谢！）

This is the output of ROAUC plot

Answer 1

好的，现在我有点明白发生了什么。

我写下这行代码看看发生了什么：

print(tpr_values)
print(fpr_values)

输出：

(0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855, 0.42857142857142855)
(0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006, 0.09575713240673006)

所有值都相同。所以他们都专注于同一点。

我解决了自己的问题。现在输出正常了：

错误就在这里：

roc_values = []
for thresh in np.linspace(0, 1, 100):
    preds = get_preds(thresh, probas)
    tn, fp, fn, tp = confusion_matrix(y_test, log_preds).ravel()
    tpr = tp/(tp+fn)
    fpr = fp/(fp+tn)
    roc_values.append([tpr, fpr])
tpr_values, fpr_values = zip(*roc_values)

在我修复 log_preds 之后，它看起来像这样：

roc_values = []
for thresh in np.linspace(0, 1, 100):
    preds = get_preds(thresh, probas)       #~~~~~~~~
    tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()
    tpr = tp/(tp+fn)                        #^^^^^^^^
    fpr = fp/(fp+tn)
    roc_values.append([tpr, fpr])
tpr_values, fpr_values = zip(*roc_values)

这很令人沮丧，但无论如何，它终于奏效了。