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

Question

I have a data set that has 95 rows and 9 columns and want to do a 5-fold cross-validation. In the training, the first 8 columns (features) are used to predict the ninth column. My test sets are correct, but my x training set is of size (4,19,9) when it should have only 8 columns and my y training set is (4,9) when it should have 19 rows. Am I indexing the subarrays incorrectly?

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

Welcome to Stack Overflow.

Once you created a new fold, you need to stack them row-wise using np.row_stack().

Also, I think you are slicing the array incorrectly, in Python or Numpy, the slicing behaviour is [inclusive:exclusive] thus, when you specify the slice as [0:7] you are only taking 7 columns, instead of 8 feature columns as you intended.

Similarly, if you are specifying 5 fold in your for loop, it should be range(k) which gives you [0,1,2,3,4] instead of range(k-1) which only gives you [0,1,2,3].

Modified code as such:

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')

which will print out the fold and the desired shape of training and testing sets for you:

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,)