使用重复分层KFold 5*10 cross_val_predict的概率

Question

我的目标是从5*10的分层Kfold CV中计算出95%置信区的AUC，特异性和灵敏度。我还需要阈值为 0.4 的特异性和灵敏度，以最大限度地提高灵敏度。

到目前为止，我能够为 AUC 实施它。代码如下：

seed = 42
# Grid Search
fit_intercept=[True, False]
C = [np.arange(1,41,1)]
penalty = ['l1', 'l2']
params = dict(C=C, fit_intercept = fit_intercept, penalty = penalty)
print(params)
logreg = LogisticRegression(random_state=seed)
# instantiate the grid
logreg_grid = GridSearchCV(logreg, param_grid = params , cv=5, scoring='roc_auc',  iid='False')
# fit the grid with data
logreg_grid.fit(X_train, y_train)
logreg = logreg_grid.best_estimator_
cv = RepeatedStratifiedKFold(n_splits = 5, n_repeats = 10, random_state = seed)

logreg_scores = cross_val_score(logreg, X_train, y_train, cv=cv, scoring='roc_auc')
print('LogReg:',logreg_scores.mean())

import scipy.stats
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * scipy.stats.t.ppf((1 + confidence) / 2, n-1)
return m, m-h, m+h
mean_confidence_interval(logreg_scores, confidence=0.95)

输出：(0.7964761904761904、0.7675441789148183、0.8254082020375626(

到目前为止，我真的很满意，但是我如何为概率实现这一点，以便我可以计算 FPR、TPR 和阈值？ 对于简单的 5 倍，我会这样做：

def evaluate_threshold(threshold):
print('Sensitivity(',threshold,'):', tpr[thresholds > threshold][-1])
print('Specificity(',threshold,'):', 1 - fpr[thresholds > threshold][-1])
logreg_proba = cross_val_predict(logreg, X_train, y_train, cv=5, method='predict_proba')
fpr, tpr, thresholds = metrics.roc_curve(y_train, log_proba[:,1])
evaluate_threshold(0.5)
evaluate_threshold(0.4)
#Output would be: 
#Sensitivity( 0.5 ): 0.76
#Specificity( 0.5 ): 0.7096774193548387
#Sensitivity( 0.4 ): 0.88
#Specificity( 0.4 ): 0.6129032258064516

如果我用 5*10 CV 尝试这样：

cv = RepeatedStratifiedKFold(n_splits = 5, n_repeats = 10, random_state = seed)    
y_pred = cross_val_predict(logreg, X_train, y_train, cv=cv, method='predict_proba')
fpr, tpr, thresholds = metrics.roc_curve(y_train, log_proba[:,1])
evaluate_threshold(0.5)
evaluate_threshold(0.4)

它抛出一个错误：

cross_val_predict only works for partitions

你能帮我解决这个问题吗？

Answer 1

这就是我尝试过的。

for i in range(10):
cv = StratifiedKFold(n_splits = 5, random_state = i)   
y_pred = cross_val_predict(logreg, X_train, y_train, cv=cv, method='predict_proba')
fpr, tpr, thresholds = metrics.roc_curve(y_train, log_proba[:,1])
evaluate_threshold(0.5)
Out: 
Sensitivity( 0.5 ): 0.84
Specificity( 0.5 ): 0.6451612903225806
Sensitivity( 0.5 ): 0.84
Specificity( 0.5 ): 0.6451612903225806
Sensitivity( 0.5 ): 0.84
Specificity( 0.5 ): 0.6451612903225806
and so on....

不幸的是，输出总是相同的，而不是我在使用RepeatedStratifiedKFold时所期望的。

也许有人可以给我一个建议？