Describe the issue linked to the documentation
I couldn't find a mention of this in docs but it appears that TorchForecastingModel doesn't use encoders when using fit/predict_from_dataset - it doesn't warn about it either during the call. This seems like a potentially big blind side for the user since it is a silent behavior. Since this method is pretty well documented otherwise, this seems like an unexpected caveat.
I wonder if this should be documented OR actually move encoder logic to be included in the _from_dataset method calls.
Sidenote:
This leads to issues with TFTModel since it just skips training and errors on predict if datasets don't contain future_covariates.
Sidenote2: categorical feature embeddings with TFTModel are also not working as expected (essentially ignored AFAIK) when using the _from_dataset methods since the code uses feature integer index instead of names for lookups but this seems like a completely separate issue.
Describe the issue linked to the documentation
I couldn't find a mention of this in docs but it appears that
TorchForecastingModeldoesn't use encoders when using fit/predict_from_dataset - it doesn't warn about it either during the call. This seems like a potentially big blind side for the user since it is a silent behavior. Since this method is pretty well documented otherwise, this seems like an unexpected caveat.I wonder if this should be documented OR actually move encoder logic to be included in the _from_dataset method calls.
Sidenote:
This leads to issues with TFTModel since it just skips training and errors on predict if datasets don't contain future_covariates.
Sidenote2: categorical feature embeddings with TFTModel are also not working as expected (essentially ignored AFAIK) when using the _from_dataset methods since the code uses feature integer index instead of names for lookups but this seems like a completely separate issue.