scaler about ukaeagroupproject HOT 6 CLOSED

Hi Lorenzo,

The scaler object is being stored as a class attribute (see here) so after you call scale() on the training data the scaler object is saved. Then when the validation and testing dataloaders are created they will be created with the training scaler already as an attribute. This means that calling scale() on the validation and test does use the same scaler object as the training.

The own scaler option is there in case you aren't using the prepare_model function and wish to set everything up yourself and you might want to pass a scaler object you have previously saved.

from ukaeagroupproject.

Ah, I see. I'm gonna test a small thing on that soon.

On another note, I'm getting the following error:

  File "/lustre/home/pr5739/qualikiz/UKAEAGroupProject/src/scripts/train_LZ.py", line 49, in main
    comet_logger, train_data, val_data, test_data = prepare_model(
  File "/lustre/home/pr5739/qualikiz/UKAEAGroupProject/src/scripts/utils.py", line 91, in prepare_model
    train_data.scale(categorical_keys=categorical_keys)
TypeError: scale() got an unexpected keyword argument 'categorical_keys'

Does that look right?

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Ah, yes sorry, the function was changed recently to allow the option to pass in variables that aren't to be scaled so the corresponding scale functions in the datasets needed to be updated. I had the changes in my branch but they weren't in main, I have now put the changes in main, check if it works now.

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ah gotcha, but shouldn't you pass ignore=categorical_keys in the scaler then?

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Technically yes, I think as it's not doing anything in the QLKNN and AutoEncoder datasets atm but neither of these problems get passed any categorical values. But yes, that should be fixed for completeness.

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ok no worries, not critical atm!

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