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Version 0.7.0
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@ottonemo ottonemo released this 29 Nov 10:40
· 300 commits to master since this release
v0.7.0
4097e90

Version 0.7.0

Notable additions are TensorBoard support through a callback and several improvements to the NeuralNetClassifier and NeuralNetBinaryClassifier to make them more compatible with sklearn metrics and packages by adding support for class inference among other things. We are actively pursuing some bigger topics which did not fit in this release such as scoring caching improvements (#557), a DataFrameTransformer (#507) and improvements to the training loop layout (#564) which we hope to bring to the next release.

WARNING: In a future release, the behavior of method net.get_params will change 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. Note that 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)

We had an influx of new contributors and users whom we thank for their support by adding pull requests and filing issues! Most notably, thanks to the individual contributors that made this release possible:

  • Alexander Kolb
  • Janaki Sheth
  • Joshy Cyriac
  • Matthias Gazzari
  • Sergey Alexandrov
  • Thomas Fan
  • Zhao Meng

Here is a list of all the changes and their coresponding ticket numbers in detail:

Added

  • More careful check for wrong parameter names being passed to NeuralNet (#500)
  • More helpful error messages when trying to predict using an uninitialized model
  • Add TensorBoard callback for automatic logging to tensorboard
  • Make NeuralNetBinaryClassifier work with sklearn.calibration.CalibratedClassifierCV
  • Improve NeuralNetBinaryClassifier compatibility with certain sklearn metrics (#515)
  • NeuralNetBinaryClassifier automatically squeezes module output if necessary (#515)
  • NeuralNetClassifier now has a classes_ attribute after fit is called, which is inferred from y by default (#465, #486)
  • NeuralNet.load_params with a checkpoint now initializes when needed (#497)

Changed

  • Improve numerical stability when using NLLLoss in NeuralNetClassifer (#491)
  • Refactor code to make gradient accumulation easier to implement (#506)
  • NeuralNetBinaryClassifier.predict_proba now returns a 2-dim array; to access the "old" y_proba, take y_proba[:, 1] (#515)
  • net.history is now a property that accesses net.history_, which stores the History object (#527)
  • Remove deprecated skorch.callbacks.CyclicLR, use torch.optim.lr_scheduler.CyclicLR instead

Future Changes

  • WARNING: In a future release, the behavior of method net.get_params will change 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. Note that 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)

Fixed

  • Fixed a bug that caused LoadInitState not to work with TrainEndCheckpoint (#528)
  • Fixed NeuralNetBinaryClassifier wrongly squeezing the batch dimension when using batch_size = 1 (#558)
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