Simplified API

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:

  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)

Added

• Improved validation of input parameters of TimeSeriesDataSet (#518)

Fixed

• Fix quantile prediction for tensors on GPUs for distribution losses (#491)
• Fix hyperparameter update for RecurrentNetwork.from_dataset method (#497)