Expectation when cloning a net #521
Comments
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One possibility I could imagine: We don't change the behavior of Unfortunately, it's not really possible to slap a deprecation warning onto Overall, most people shouldn't be affected by the change, but I can imagine it leads to some surprising bugs, as many are not even aware when |
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I agree with being compatible with the sklearn api of
I do not think pickling will be affected by this change. Pickling serializes all the attribute regardless of how
Since grid search calls |
That would be my biggest worry with the current situation, especially since it's not easy to notice.
Pickling will probably not be a problem but still some code could break because of the change. |
This release of skorch contains a few minor improvements and some nice additions. As always, we fixed a few bugs and improved the documentation. Our [learning rate scheduler](https://skorch.readthedocs.io/en/latest/callbacks.html#skorch.callbacks.LRScheduler) now optionally logs learning rate changes to the history; moreover, it now allows the user to choose whether an update step should be made after each batch or each epoch. If you always longed for a metric that would just use whatever is defined by your criterion, look no further than [`loss_scoring`](https://skorch.readthedocs.io/en/latest/scoring.html#skorch.scoring.loss_scoring). Also, skorch now allows you to easily change the kind of nonlinearity to apply to the module's output when `predict` and `predict_proba` are called, by passing the `predict_nonlinearity` argument. Besides these changes, we improved the customization potential of skorch. First of all, the `criterion` is now set to `train` or `valid`, depending on the phase -- this is useful if the criterion should act differently during training and validation. Next we made it easier to add custom modules, optimizers, and criteria to your neural net; this should facilitate implementing architectures like GANs. Consult the [docs](https://skorch.readthedocs.io/en/latest/user/neuralnet.html#subclassing-neuralnet) for more on this. Conveniently, [`net.save_params`](https://skorch.readthedocs.io/en/latest/net.html#skorch.net.NeuralNet.save_params) can now persist arbitrary attributes, including those custom modules. As always, these improvements wouldn't have been possible without the community. Please keep asking questions, raising issues, and proposing new features. We are especially grateful to those community members, old and new, who contributed via PRs: ``` Aaron Berk guybuk kqf Michał Słapek Scott Sievert Yann Dubois Zhao Meng ``` Here is the full list of all changes: ### Added - Added the `event_name` argument for `LRScheduler` for optional recording of LR changes inside `net.history`. NOTE: Supported only in Pytorch>=1.4 - Make it easier to add custom modules or optimizers to a neural net class by automatically registering them where necessary and by making them available to set_params - Added the `step_every` argument for `LRScheduler` to set whether the scheduler step should be taken on every epoch or on every batch. - Added the `scoring` module with `loss_scoring` function, which computes the net's loss (using `get_loss`) on provided input data. - Added a parameter `predict_nonlinearity` to `NeuralNet` which allows users to control the nonlinearity to be applied to the module output when calling `predict` and `predict_proba` (#637, #661) - Added the possibility to save the criterion with `save_params` and with checkpoint callbacks - Added the possibility to save custom modules with `save_params` and with checkpoint callbacks ### Changed - Removed support for schedulers with a `batch_step()` method in `LRScheduler`. - Raise `FutureWarning` in `CVSplit` when `random_state` is not used. Will raise an exception in a future (#620) - The behavior of method `net.get_params` changed to make it more consistent with sklearn: it will no longer return "learned" attributes like `module_`; therefore, functions like `sklearn.base.clone`, when called with a fitted net, will no longer return a fitted net but instead an uninitialized net; if you want a copy of a fitted net, use `copy.deepcopy` instead;`net.get_params` is used under the hood by many sklearn functions and classes, such as `GridSearchCV`, whose behavior may thus be affected by the change. (#521, #527) - Raise `FutureWarning` when using `CyclicLR` scheduler, because the default behavior has changed from taking a step every batch to taking a step every epoch. (#626) - Set train/validation on criterion if it's a PyTorch module (#621) - Don't pass `y=None` to `NeuralNet.train_split` to enable the direct use of split functions without positional `y` in their signatures. This is useful when working with unsupervised data (#605). - `to_numpy` is now able to unpack dicts and lists/tuples (#657, #658) - When using `CrossEntropyLoss`, softmax is now automatically applied to the output when calling `predict` or `predict_proba` ### Fixed - Fixed a bug where `CyclicLR` scheduler would update during both training and validation rather than just during training. - Fixed a bug introduced by moving the `optimizer.zero_grad()` call outside of the train step function, making it incompatible with LBFGS and other optimizers that call the train step several times per batch (#636) - Fixed pickling of the `ProgressBar` callback (#656)
At the moment, when we use
sklearn.base.cloneon a fitted net, we receive a fitted net. In sklearn, however, we get back an unfitted estimator. The main reason is that outnet.get_params()also returns learned attributes (ending on_, likemodule_).For sklearn compliance, we might want to change this behavior. However, I wonder if this can have some unintended side-effects are might break backwards-compatibility for too many people (say, making it so that old pickle files no longer work?).
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