Question

我关注Kaggle上的其中一个内核，主要是关注A kernel for Credit Card Fraud Detection。

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 （）获得了参数＆＃39; shuffle＆＃39;
的多个值

如果我从此行中删除shuffle=False，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，我会收到以下错误;

TypeError：＆＃39; KFold＆＃39;对象不可迭代来自这一行：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，那么我们将非常感激。

感谢。

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

那么你的代码应该可行。因为它需要3个参数： - 数组长度，分割数和随机数

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在enumerate()中进行更改。

顺便说一句，model_selection是新模块，建议使用。尝试使用它：

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)

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

如果你想获得训练/测试循环的索引，只需添加枚举

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

TypeError：＆＃39; KFold＆＃39;对象不可迭代

2 个答案: