我的目标是计算5*10 StratifiedKfold CV的AUC、特异性、灵敏度和95%CI。我还需要在阈值为0.4的情况下计算出特异性和灵敏度,以使灵敏度最大化。
到目前为止,我能够实现它的AUC。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,但我如何实现这个概率,以便计算FPR,TPR和阈值?对于一个简单的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的CV试试这个方法,
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)
就会出现错误
cross_val_predict only works for partitions
你能帮我解决这个问题吗?
这就是我所尝试的。
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....
遗憾的是,当我使用RepeatedStratifiedKFold时,输出结果总是一样的,而且不是我所期望的。
也许有人能给我一个建议?