Closed mahdiebm99ipm closed 3 years ago
Interesting. @diditforlulz273 had a similar problem. As I cannot reproduce the issue, could you provide more details of the tensor that lacks the grad_fn? An example in a colab notebook would be worth gold.
Indeed. But this error suddenly disappeared since I upgraded pytorch-forecasting to 0.7.0, so I can't provide any example, unfortunately.
@jdb78 thanks for your response, I will provide complete example on Anonymized dataset and share it. the problem occurs when number of unique time series are large. I have tested the code on small set of data and it worked.
Not sure if it might help narrow down the issue ore if I'm just piggybacking off of an actual issue here, but I came across the same error after trying to apply the code from your TFT tutorial to OWID's COVID19 dataset in an attempt to build a model for COVID19 forecasting (see end of the notebook)
I also encountered this error. No solutions yet.
A reproducible example in a colab notebook would help a lot to solve the issue.
@jdb78 would this help? It's the first time I use colab, so feel free to lmk if you need anything else.
The specific error is caused by missings in the data. allow_missings
in the TimeSeriesDataSet refers to missing observations in the sense of that a complete row is not in the dataset. Categorical missings can be treated by using the NaNLabelEncoder(add_nan=True)
but continuous variables need to be free of nulls (particularly the target). I will clarify the documentation and add some asserts in the code.
The specific error is caused by missings in the data.
allow_missings
in the TimeSeriesDataSet refers to missing observations in the sense of that a complete row is not in the dataset. Categorical missings can be treated by using theNaNLabelEncoder(add_nan=True)
but continuous variables need to be free of nulls (particularly the target). I will clarify the documentation and add some asserts in the code.
thanks a lot for the clarification @jdb78 ! curious to hear whether the others had the same root cause.
The specific error is caused by missings in the data.
allow_missings
in the TimeSeriesDataSet refers to missing observations in the sense of that a complete row is not in the dataset. Categorical missings can be treated by using theNaNLabelEncoder(add_nan=True)
but continuous variables need to be free of nulls (particularly the target). I will clarify the documentation and add some asserts in the code.thanks a lot for the clarification @jdb78 ! curious to hear whether the others had the same root cause.
yes,i get the error by the same cause. Thanks a lot
Hi, I'm trying to follow the tutorial with my own data. When I run the learning rate finder, i got this error:
here is the full traceback: