Add step_args parameter to LRScheduler.simulate#1077
Add step_args parameter to LRScheduler.simulate#1077githubnemo merged 3 commits intoskorch-dev:masterfrom
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The purpose of this change is to allow for simulation of scheduling policies such as `ReduceLROnPlateau` to be simulated properly. If `step_args` is an indexable object, it is indexed using the current simulated epoch to get closer to real-life behavior, e.g. simulating a real loss curve and the corresponding behavior of the LR scheduler policy.
BenjaminBossan
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Thanks for the PR to enhance the capabilities of simulate. My comments are mostly about clarifications.
Failing tests require #1078 to be merged.
| steps=5, initial_lr=1, step_args=0.5 | ||
| ) | ||
| # O = OK epoch | ||
| # I = intertolerable epoch |
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What is "intertolerable"?
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This is terminology taken from the docs
In the second epoch, if the performance is worse than the baseline, we have what is considered an intolerable epoch.
Drastic terminology but I wanted to adhere to already defined terms :)
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Drastic indeed, but it's in the Shakespeare corpus, so it must be a common English word.
| # O I I I I epoch classification | ||
| # 0 1 2 3 4 number of bad epochs | ||
| # * * * epochs with LR reduction |
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Should it be:
| # O I I I I epoch classification | |
| # 0 1 2 3 4 number of bad epochs | |
| # * * * epochs with LR reduction | |
| # O I I I I epoch classification | |
| # 0 1 2 3 4 number of bad epochs | |
| # * epochs with LR reduction |
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No, it's actually
# O I I I I epoch classification
# 0 1 2 1 2 number of bad epochs
# * * epochs with LR reductionI've updated the comment but the gist is that the bad epoch counter is reset after an LR reduction.
| # note that simulate returns the LRs before the step, not after, | ||
| # so we're seeing only 4 updated values. | ||
| assert all(lrs == [1] + [1, 1, 0.1, 0.1]) |
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Do we see "4 updated values"? Why use [1] + ... here but not in the next test?
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I've removed the [1], it did not help visualize what's going on. I've also changed it to "4 simulated values" since the first value to return is always the init. LR.
| initial_lr: float | ||
| Initial learning rate | ||
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| step_args: Any |
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Would it be more helpful to change the type to None or float or list of float (default=None)?
Yes, technically it doesn't have to be a list but I don't think it still makes it easier for users to know what they need to pass.
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Agreed. Changed!
| step_args: Any | ||
| Argument to the `.step()` function of the policy. If it is an | ||
| indexable object the simulation will try to associate every step of | ||
| the simulation with an entry in ``step_args``. |
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Also describe what happens when None or float are passed?
BenjaminBossan
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Thanks for the changes, it's now much easier to understand what happens.
Please welcome skorch 1.1.0 - a smaller release with a few fixes, a new notebook showcasing learning rate schedulers and mainly support for scikit-learn 1.6.0. Full list of changes: ### Added - Added a [notebook](https://github.com/skorch-dev/skorch/blob/master/notebooks/Learning_Rate_Scheduler.ipynb) that shows how to use Learning Rate Scheduler in skorch.(#1074) ### Changed - All neural net classes now inherit from sklearn's [`BaseEstimator`](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html). This is to support compatibility with sklearn 1.6.0 and above. Classification models additionally inherit from [`ClassifierMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html) and regressors from [`RegressorMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html). (#1078) - When using the `ReduceLROnPlateau` learning rate scheduler, we now record the learning rate in the net history (`net.history[:, 'event_lr']` by default). It is now also possible to to step per batch, not only by epoch (#1075) - The learning rate scheduler `.simulate()` method now supports adding step args which is useful when simulation policies such as `ReduceLROnPlateau` which expect metrics to base their schedule on. (#1077) - Removed deprecated `skorch.callbacks.scoring.cache_net_infer` (#1088) ### Fixed - Fix an issue with using `NeuralNetBinaryClassifier` with `torch.compile` (#1058)
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The purpose of this change is to allow for simulation of scheduling policies such as
ReduceLROnPlateauto be simulated properly.If
step_argsis an indexable object, it is indexed using the current simulated epoch to get closer to real-life behavior, e.g. simulating a real loss curve and the corresponding behavior of the LR scheduler policy.