python – TypeError：’KFold’对象不可迭代

我到达了需要执行KFold的步骤,以便找到Logistic回归的最佳参数.

以下代码显示在内核本身,但由于某种原因(可能是旧版本的scikit-learn,给我一些错误).

def printing_Kfold_scores(x_train_data,y_train_data):
    fold = KFold(len(y_train_data),5,shuffle=False) 

    # Different C parameters
    c_param_range = [0.01,0.1,1,10,100]

    results_table = pd.DataFrame(index = range(len(c_param_range),2),columns = ['C_parameter','Mean recall score'])
    results_table['C_parameter'] = c_param_range

    # the k-fold will give 2 lists: train_indices = indices[0],test_indices = indices[1]
    j = 0
    for c_param in c_param_range:
        print('-------------------------------------------')
        print('C parameter: ',c_param)
        print('-------------------------------------------')
        print('')

        recall_accs = []
        for iteration,indices in enumerate(fold,start=1):

            # Call the logistic regression model with a certain C parameter
            lr = LogisticRegression(C = c_param,penalty = 'l1')

            # Use the training data to fit the model. In this case,we use the portion of the fold to train the model
            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]
            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())

            # Predict values using the test indices in the training data
            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)

            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter
            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)
            recall_accs.append(recall_acc)
            print('Iteration ',iteration,': recall score = ',recall_acc)

            # The mean value of those recall scores is the metric we want to save and get hold of.
        results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)
        j += 1
        print('')
        print('Mean recall score ',np.mean(recall_accs))
        print('')

    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']

    # Finally,we can check which C parameter is the best amongst the chosen.
    print('*********************************************************************************')
    print('Best model to choose from cross validation is with C parameter = ',best_c)
    print('*********************************************************************************')

    return best_c

我得到的错误如下：
对于这一行：fold = KFold(len(y_train_data),shuffle = False)
错误：

TypeError: init() got multiple values for argument ‘shuffle’

如果我从这一行中删除shuffle = False,我收到以下错误：

TypeError: shuffle must be True or False; got 5

如果我删除5并保持shuffle = False,我收到以下错误;

TypeError: ‘KFold’ object is not iterable
which is from this line: for iteration,start=1):

如果有人可以帮助我解决这个问题,并建议如何使用最新版本的scikit-learn来完成,我们将非常感激.

谢谢.