v0.2.3

New functionalities

1. The ability to condition (i)VAE on both continuous and discrete variables. The latter usually represent categorical data classes (aka labels) whereas the former can represent some continuous property (or multiple properties) of the data that we know. See the example notebook.

2. (i)VAE for semi-supervised regression. To date, most applications of the semi-supervised VAE were for categorical data. Here we introduce an option to use semi-supervised VAE for regression analysis where for some (small) part of the data both the label variable and the predictors are observed, while for other (larger) part of the data only the predictors are given. The usage of the ss_reg_iVAE class is similar to that of the ssiVAE

3. The auxSVITrainer now works for both classification and regression tasks. To choose between the two, use the task argument (e.g. task="classification" or task=regression```).

Breaking changes

1. The way categorical variables are passed to ivae.manifold2d has changed.

Before:

for i in range(10):
    cvae.manifold2d(d=12, label=i, cmap="viridis")

Now:

for i in range(10):
    cls = pv.utils.to_onehot(torch.tensor([i,]), 10)
    cvae.manifold2d(d=12, y=cls, cmap="viridis")

2. pyroVED < 0.2.3 is not compatible with the latest PyTorch version 1.9. Hence, if using earlier pyroVED versions, downgrade to the PyTorch < 1.9. See this page for details.