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@PonteIneptique
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Actually duplicates in part #53 because I forgot to go back to master...

This adds the ability to chose on which metric to fit in regard to devset: accuracy, f1, precision, recall, balanced-accuracy.

Default value should not change pas behaviours.

Now, instead of the default accuracy for schedule, one can choose precision, recall, balanced-accuracy or f1
as a metric for devset fitting.
@PonteIneptique
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The LR Scheduler does not seem to update with this PR. I need to get into the details here but I got 25 steps for lemmatization with lr_patience 10 and patience 8 which definitely does not seem right.

@emanjavacas
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Did you find out why?

@PonteIneptique
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I'll go back into having a look at this.

@PonteIneptique
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This is good to go.

@PonteIneptique
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I was a bit surprised (also) by the fact we still print threshold value of LROnPlateau but we do not set it.

pie/pie/trainer.py

Lines 202 to 204 in 230e0e6

self.lr_scheduler = LRScheduler(
self.optimizer, factor=settings.lr_factor,
patience=settings.lr_patience, min_lr=settings.min_lr)

pie/pie/trainer.py

Lines 154 to 155 in 230e0e6

self.lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', threshold=threshold, **kwargs)

It feels to me we should forward the value of the target task but :)

@emanjavacas
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What was the issue?

Re: threshold. You are right. I never got to use it and was kept a 0.0. However, note that there are 2 thresholds here. One is for the LR schedule, the other is for the task schedule. Just saying because it's not very well documented...

@PonteIneptique
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PonteIneptique commented Aug 4, 2020 via email

@PonteIneptique
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Note: I think if you merge this Optuna will need to be changed a litte :)

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2 participants