Deep-Learning-with-PyTorch

Representing tabular data
83
[6],
...,
[7],
[6]])
The call to unsqueeze adds a singleton dimension, from a 1D tensor of 4,898 elements
to a 2D tensor of size (4,898 × 1), without changing its contents—no extra elements
are added; we just decided to use an extra index to access the elements. That is, we
access the first element of target as target[0] and the first element of its
unsqueezed counterpart as target_unsqueezed[0,0].
PyTorch allows us to use class indices directly as targets while training neural networks.
However, if we wanted to use the score as a categorical input to the network, we
would have to transform it to a one-hot-encoded tensor.
4.3.5 When to categorize
Now we have seen ways to deal with both continuous and categorical data. You may
wonder what the deal is with the ordinal case discussed in the earlier sidebar. There is
no general recipe for it; most commonly, such data is either treated as categorical (losing
the ordering part, and hoping that maybe our model will pick it up during training
if we only have a few categories) or continuous (introducing an arbitrary notion of
distance). We will do the latter for the weather situation in figure 4.5. We summarize
our data mapping in a small flow chart in figure 4.4.
Column
contains
Example representation
of one value
Continuous
Data
yes
Use values directly
3.1415
no
yes
Treat as continuous
Ordinal
Data
yes
ordering
a priority?
no
no
Treat as categorical
Categorical
Data
yes
Use one-hot
or embeDding
0 0 0 0 1 0 0 0 0
Figure 4.4
How to treat columns with continuous, ordinal, and categorical data