TypeError:'KFold'对象不可迭代

Question

我正在关注Kaggle的一个内核,主要是,我正在关注信用卡欺诈检测的内核.

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),5,shuffle=False) 错误:

TypeError:init()为参数'shuffle'获取了多个值

如果我shuffle=False从这一行删除,我收到以下错误:

TypeError:shuffle必须为True或False; 得到5

如果我删除5并保留shuffle=False,我收到以下错误;

TypeError:'KFold'对象不可迭代,来自此行: 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来完成,我们将非常感激.

谢谢.

Answer 1

这取决于您如何导入KFold.

如果你这样做了:

from sklearn.cross_validation import KFold

然后你的代码应该工作.因为它需要3个参数: - 数组的长度,分割的数量和随机播放

但如果你这样做:

from sklearn.model_selection import KFold

然后这将无法工作,你只需要传递分裂数和随机数.无需传递数组的长度以及进行更改enumerate().

顺便说一下,model_selection是新模块并建议使用.尝试使用它像这样:

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)

Answer 2

KFold是一个分裂者,所以你必须给分裂.

示例代码:

X = np.array([1,1,1,1], [2,2,2,2], [3,3,3,3], [4,4,4,4]])
y = np.array([1, 2, 3, 4])
# Now you create your Kfolds by the way you just have to pass number of splits and if you want to shuffle.
fold = KFold(2,shuffle=False)
# For iterate over the folds just use split
for train_index, test_index in fold.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    # Follow fitting the classifier

如果你想获得train/test循环的索引,只需添加enumerate

for i, train_index, test_index in enumerate(fold.split(X)):
    print('Iteration:', i)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

我希望这有效