我正在跟踪Kaggle上的内核之一，主要是在跟踪A kernel for Credit Card Fraud Detection。
我到达了需要执行KFold以便找到Logistic回归的最佳参数的步骤。
以下代码显示在内核本身中，但是由于某些原因(可能是scikit-learn的较旧版本，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:
对于此行:
fold = KFold(len(y_train_data),5,shuffle=False)错误:如果我从此行中删除
shuffle=False，则会收到以下错误消息:如果删除
5并保留shuffle=False，则会出现以下错误；如果有人可以帮助我解决此问题并提出如何使用最新版本的scikit-learn做到这一点，将不胜感激。
谢谢。
最佳答案
KFold是拆分器，因此您必须提供一些拆分方法。
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
如果要获取训练/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]
我希望这能奏效