假设我在joblib中实现了以下循环：
from sklearn.model_selection import KFold
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix, f1_score
from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import LinearSVC
from joblib import Parallel, delayed
iris = datasets.load_iris()
X, Y = iris.data, iris.target
skf = StratifiedKFold(n_splits=5)
clf = svm.SVC(kernel='rbf')
def train(train_index, test_index):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = Y[train_index], Y[test_index]
clf.fit(X_train, y_train)
Y_predict = clf.predict(X_test)
f1 = f1_score(y_test, Y_predict, average='weighted')
conf_mat = confusion_matrix(y_test, Y_predict)
return dict(f1=f1, conf_mat=conf_mat)
out = Parallel(n_jobs=2, verbose=100, pre_dispatch='1.5*n_jobs')(
delayed(train)(train_index, test_index) for train_index, test_index in skf.split(X, Y))
f1_scores = [d['f1'] for d in out]
conf_mats = [d['conf_mat'] for d in out]
print('f1_scores:', f1_scores)
print('confusion matrices:', conf_mats)
我想检查循环中的条件：
对于train_index，skf.split（X，Y）中的test_index
如果满足条件，则在下一个迭代中继续。例如，如果我想跳过火车和测试数组的索引（如果想精确的话，则为＃5）。
如本代码所示，如何在joblib上下文中实现Python continue？