Cleanup config files

[sfluegel05]

We have a lot of config files that do different things which might be confusing to new users and lead to unexpected results. For example, the callback configs sometimes save a checkpoint every 25 epochs, sometimes they only save the last checkpoint. Sometimes, they save the best 3, sometimes only the best 1.

I tried to reduce the number of configs and find a consistent naming pattern. More precisely, I

• removed the weightings directory and loss/weighting_chebi100.yml - those were all static lists of weights which are not needed anymore since those weights get calculated on the fly by the weighted BCE.
• removed the metrics which had a specific label number attached. Those label numbers are automatically inferred from the dataset
• restored the default trainer and default callbacks to their original state (which means removing the roc-auc). This fixes Missing labels in loss kwargs causing issue for electra model / Issues from PR#130 #140 (comment). For other callbacks, the roc-auc is still used (e.g. binary_callbacks.yml and early_stop_callbacks_roc-auc.yml
• renamed the solCur and tox21 callbacks (as far as I can tell, they are not specific to the dataset)
• removed some comments (I assume that those parts are outdated)
• added an upper limit of 1 to the weighted BCE's beta parameter

solves #139

[sfluegel05]

@schnamo Could you please have a look if this makes sense to you or if I have removed anything important?

[sfluegel05]

One more fix: The current_epoch parameter only works if a loss function expects it (or at least ignores unknown kwargs). This is not the case for the unweighted BCE loss. Fortunately, we can use the pass_loss_kwargs flag to avoid this issue.

[aditya0by0]

python-chebai/configs/loss/bce_unweighted.yml

Line 1 in 395b5a8

class_path: torch.nn.BCEWithLogitsLoss

I think the raw torch module for BCE loss will throw an error, as we pass some kwargs to forward method which is not accepted by it.

Instead the config should use the wrapper from

python-chebai/chebai/loss/bce_weighted.py

Lines 108 to 111 in 395b5a8

	class UnWeightedBCEWithLogitsLoss(torch.nn.BCEWithLogitsLoss):
	def forward(self, input, target, **kwargs):
	# As the custom passed kwargs are not used in BCEWithLogitsLoss, we can ignore them
	return super().forward(input, target)

[schnamo]

Looks great, thank you Simon!

[schnamo]

Everything looks good, thank you

[aditya0by0]

@sfluegel05
Passing complete file path causes following issue: https://wandb.ai/chebai/chebai/runs/pjql8wjk/logs?nw=nwuseraditya0by0

Passing just the file name causes the below issue
https://wandb.ai/chebai/chebai/runs/m5rgurmi/logs?nw=nwuseraditya0by0

[aditya0by0]

Do we need to retain certain comments from PR #130? ... like below

python-chebai/chebai/cli.py

Lines 87 to 96 in 395b5a8

	# parser.link_arguments(
	# "model.init_args.out_dim", "trainer.callbacks.init_args.num_labels"
	# )
	# parser.link_arguments(
	# "data", "model.init_args.criterion.init_args.data_extractor"
	# )
	# parser.link_arguments(
	# "data.init_args.chebi_version",
	# "model.init_args.criterion.init_args.data_extractor.init_args.chebi_version",
	# )

python-chebai/chebai/cli.py

Lines 73 to 77 in 395b5a8

	# When using lightning > 2.5.1 then need to uncomment all metrics that are not used
	# for average in ("mse", "rmse","r2"): # for regression
	# for average in ("f1", "roc-auc"): # for binary classification
	# for average in ("micro-f1", "macro-f1", "roc-auc"): # for multilabel classification
	# for average in ("micro-f1", "macro-f1", "balanced-accuracy", "roc-auc"): # for multilabel classification using balanced-accuracy

...
...
etc