如何在没有 ski-kit learn 的情况下为 K-Fold 交叉验证创建训练集？

Question

我有一个包含 95 行和 9 列的数据集，并且想要进行 5 折交叉验证。在训练中，前 8 列（特征）用于预测第 9 列。我的测试集是正确的，但是我的 x 训练集的大小是 (4,19,9)，而它应该只有 8 列，而我的 y 训练集是 (4,9)，而它应该有 19 行。我是否错误地索引了子数组？

kdata = data[0:95,:] # Need total rows to be divisible by 5, so ignore last 2 rows 
np.random.shuffle(kdata) # Shuffle all rows
folds = np.array_split(kdata, k) # each fold is 19 rows x 9 columns

for i in range (k-1):
    xtest = folds[i][:,0:7] # Set ith fold to be test
    ytest = folds[i][:,8]
    new_folds = np.delete(folds,i,0)
    xtrain = new_folds[:][:][0:7] # training set is all folds, all rows x 8 cols
    ytrain = new_folds[:][:][8]   # training y is all folds, all rows x 1 col

Answer 1

欢迎来到 Stack Overflow。

创建新折叠后，您需要使用 np.row_stack() 将它们按行堆叠。

此外，我认为您对数组的切片不正确，在 Python 或 Numpy 中，切片行为是 [inclusive:exclusive] 因此，当您将切片指定为 [0:7] 时，您只是采用 7 列，而不是您预期的 8 列特征。

同样，如果您在 for 循环中指定 5 倍，它应该是 range(k)，它给您 [0,1,2,3,4] 而不是 range(k-1)，它只给您 [0,1,2,3] .

修改后的代码：

folds = np.array_split(kdata, k) # each fold is 19 rows x 9 columns
np.random.shuffle(kdata) # Shuffle all rows
folds = np.array_split(kdata, k)

for i in range (k):
    xtest = folds[i][:,:8] # Set ith fold to be test
    ytest = folds[i][:,8]
    new_folds = np.row_stack(np.delete(folds,i,0))
    xtrain = new_folds[:, :8]
    ytrain = new_folds[:,8]

    # some print functions to help you debug
    print(f'Fold {i}')
    print(f'xtest shape  : {xtest.shape}')
    print(f'ytest shape  : {ytest.shape}')
    print(f'xtrain shape : {xtrain.shape}')
    print(f'ytrain shape : {ytrain.shape}\n')

这将为您打印出训练集和测试集的折叠和所需形状：

Fold 0
xtest shape  : (19, 8)
ytest shape  : (19,)
xtrain shape : (76, 8)
ytrain shape : (76,)

Fold 1
xtest shape  : (19, 8)
ytest shape  : (19,)
xtrain shape : (76, 8)
ytrain shape : (76,)

Fold 2
xtest shape  : (19, 8)
ytest shape  : (19,)
xtrain shape : (76, 8)
ytrain shape : (76,)

Fold 3
xtest shape  : (19, 8)
ytest shape  : (19,)
xtrain shape : (76, 8)
ytrain shape : (76,)

Fold 4
xtest shape  : (19, 8)
ytest shape  : (19,)
xtrain shape : (76, 8)
ytrain shape : (76,)

如何在没有 ski-kit learn 的情况下为 K-Fold 交叉验证创建训练集？

How to create Training Sets for K-Fold Cross Validation without ski-kit learn?

python

numpy

machine-learning

cross-validation

k-fold