Swaps Validation set with a section of Training set, given a value for k

kfold_swap.py

# In case you didn't think to add k-fold cross-validation until late in your
# ML project,...
# This is built for a situation where datasets are arrays of, say, images. 

def kfold_swap(train_X, train_Y, val_X, val_Y, k):
    """
    Swaps val with a section of train, given a value for k
    "Duct tape" approach used to "retro-fit" k-fold cross-validation while minimally 
        disturbing the rest of the code, while avoiding reloading data from disk and 
        keeping RAM use manageable. (e.g. np.append() is bad b/c it would copy all of train)
    "Not-quite in-place" swapping means only a val-sized section of train gets duplicated in storage. 
    For 80-20 train/val split, k can run from 0 to 4 (= 5-fold cross-val)
    For 80-10-10 train/val/test split, k can run from 0 to 8 (= 9-fold cross-val)
    For 70-15-15 train/val/test split, exceeding k=4 will give you a failed assertion
    """
    if k > 0:             # k=0 means do nothing
        vl = val_X.shape[0]

        # sanity checks: make sure sizes are ok
        assert train_X.shape[0] == train_Y.shape[0]
        assert val_X.shape[0]   == val_Y.shape[0]
        assert k*vl <= train_X.shape[0]

        bgn, end = (k-1)*vl, k*vl # minus sign is from choice that k=0 is no-op
        val_X, train_X[bgn:end,:,:] = (train_X[bgn:end,:,:]).copy(), val_X
        val_Y, train_Y[bgn:end,:,:] = (train_Y[bgn:end,:,:]).copy(), val_Y

    return train_X, train_Y, val_X, val_Y