为每个分层测试拆分生成和组装模型预测

Question

我想使用分层KFOLD（SKF）生成多个测试数据拆分，然后使用SKLEARNN模型为这些测试数据拆分（以及所有数据）生成/组装预测。我愿意以编程方式进行此操作。

我已经使用下面的最小数据示例重新捕获了我的代码。简而言之，（在数据导入之后），我具有一个拟合模型并生成模型预测概率的函数。随后，我尝试将此功能传递给数据的每个SKF分开，以生成并整理我数据的每一行的预测概率。但是，此步骤失败并生成一个值（预期的布尔数组）。我的代码如下：

from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold

    #load data, assemble dataframe
iris = datasets.load_iris()
X = pd.DataFrame(iris.data[51:150, :], columns = ["sepal_length", "sepal_width", 
"petal_length", "petal_width"])
y = pd.DataFrame(iris.target[51:150,], columns = ["target"])
df = pd.concat([X,y], axis = 1)

    #instantiate logistic regression
log = LogisticRegression()

    #modelling function
def train_model(train, test, fold):
    X = df.drop("target", axis = 1)
    y = df["target"]

    X_train = train[X]
    y_train = train[y]
    X_test = test[X]
    y_test = test[y]

        #generate probability of class 1 predictions from logistic regression model fit
    prob = log.fit(X_train, y_train).predict_proba(X_test)[:, 1]
    return (prob)
  #generate straified k-fold splits (2 used as example here)
skf = StratifiedKFold(n_splits = 2)

   #generate and collate all predictions (for each row in df)
fold = 1
outputs = []
for train_index, test_index in skf.split(df, y):
    train_df = df.loc[train_index,:]
    test_df = df.loc[test_index,:]
    output = train_model(train_df,test_df,fold) #generate model probabilities for X_test 
    in skf split
    outputs.append(output) #append all model probabilities 
    fold = fold + 1  

all_preds = pd.concat(outputs)

有人可以指导我进入包含行索引及其预测概率的解决方案吗？