Breaking changes
- Removed `dropout_categoricals` parameter from `TimeSeriesDataSet`.
Use `categorical_encoders=dict(<variable_name>=NaNLabelEncoder(add_nan=True)`) instead (518)
- Rename parameter `allow_missings` for `TimeSeriesDataSet` to `allow_missing_timesteps` (518)
- Transparent handling of transformations. Forward methods should now call two new methods (518):
- `transform_output` to explicitly rescale the network outputs into the de-normalized space
- `to_network_output` to create a dict-like named tuple. This allows tracing the modules with PyTorch's JIT. Only `prediction` is still required which is the main network output.
Example:
python
def forward(self, x):
normalized_prediction = self.module(x)
prediction = self.transform_output(prediction=normalized_prediction, target_scale=x["target_scale"])
return self.to_network_output(prediction=prediction)
Fixed
- Fix quantile prediction for tensors on GPUs for distribution losses (491)
- Fix hyperparameter update for RecurrentNetwork.from_dataset method (497)
Added
- Improved validation of input parameters of TimeSeriesDataSet (518)