Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

There is one exception to the "always shuffle" rule, though: time
series problems, where shuffling can lead to data leakage.
Train-Validation-Test Split
It is beyond the scope of this book to explain the reasoning behind the trainvalidation-test
split, but there are two points I’d like to make:
1. The split should always be the first thing you do—no preprocessing, no
transformations; nothing happens before the split. That’s why we do this
immediately after the synthetic data generation.
2. In this chapter we will use only the training set, so I did not bother to create a
test set, but I performed a split nonetheless to highlight point #1 :-)
Train-Validation Split
1 # Shuffles the indices
2 idx = np.arange(N)
3 np.random.shuffle(idx)
4
5 # Uses first 80 random indices for train
6 train_idx = idx[:int(N*.8)]
7 # Uses the remaining indices for validation
8 val_idx = idx[int(N*.8):]
9
10 # Generates train and validation sets
11 x_train, y_train = x[train_idx], y[train_idx]
12 x_val, y_val = x[val_idx], y[val_idx]
"Why didn’t you use train_test_split() from Scikit-Learn?" you
may be asking.
That’s a fair point. Later on, we will refer to the indices of the data points belonging
to either train or validation sets, instead of the points themselves. So, I thought of
using them from the very start.
Data Generation | 27