我正在关注 Kaggle 上的一个内核，主要是我在关注A kernel for Credit Card Fraud Detection .

我到达了需要执行 KFold 以便找到逻辑回归的最佳参数的步骤。

以下代码显示在内核本身中，但出于某种原因(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),5,shuffle=False) 错误:

TypeError: init() got multiple values for argument 'shuffle'

如果我从这一行中删除 shuffle=False，我会收到以下错误:

TypeError: shuffle must be True or False; got 5

如果我删除 5 并保留 shuffle=False，我会收到以下错误；

TypeError: 'KFold' object is not iterable which is from this line: for iteration, indices in enumerate(fold,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 来解决这个问题，我们将不胜感激。

谢谢。

最佳答案

这取决于您导入 KFold 的方式。

如果你这样做了:

from sklearn.cross_validation import KFold

那么你的代码应该可以工作了。因为它需要 3 个参数:- 数组长度、拆分数和随机播放

但是如果你这样做:

from sklearn.model_selection import KFold

那么这就不行了，你只需要传递 splits 和 shuffle 的数量。在 enumerate() 中进行更改时无需传递数组的长度。

顺便说一下，model_selection 是新模块，推荐使用。尝试像这样使用它:

fold = KFold(5,shuffle=False)

for train_index, test_index in fold.split(X):

    # 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
    lr.fit(x_train_data.iloc[train_index,:], y_train_data.iloc[train_index,:].values.ravel())

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

关于python - 类型错误 : 'KFold' object is not iterable，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48641290/