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@rodrigo-arenas rodrigo-arenas released this 05 Jul 21:42
· 197 commits to master since this release
0.6.0
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Learn about vigilant mode.

This is a big release with several new features and enhancements! 🎊

Features:

  • Added the ProgressBar callback, it uses tqdm progress bar to shows how many generations are left in the training progress.

  • Added the TensorBoard callback to log the generation metrics, watch in real-time while the models are trained, and compare different runs in your TensorBoard instance.

  • Added the TimerStopping callback to stop the iterations after a total (threshold) fitting time has been elapsed.

  • Added new parallel coordinates plot using plot_parallel_coordinates by @Raul9595

  • Now if one or more callbacks decides to stop the algorithm, it will print its class name to know which callbacks were responsible of the stopping.

  • Added support for extra methods coming from scikit-learn's BaseSearchCV, like cv_results_,
    best_index_ and refit_time_ among others.

  • Added methods on_start and on_end to BaseCallback. Now the algorithms check for the callbacks like this:

    • on_start: When the evolutionary algorithm is called from the GASearchCV.fit method.

    • on_step: When the evolutionary algorithm finishes a generation (no change here).

    • on_end: At the end of the last generation.

Bug Fixes:

  • A missing statement was making that the callbacks start to get evaluated from generation 1, ignoring generation 0. Now this is properly handled and callbacks work from generation 0.

API Changes:

  • The modules sklearn_genetic.plots and sklearn_genetic.mlflow.MLflowConfig now requires an explicit installation of seaborn and mlflow, now those are optionally installed using pip install sklearn-genetic-opt[all].
  • The GASearchCV.logbook property now has extra information that comes from the scikit-learn cross_validate function.
  • An optional extra parameter was added to GASearchCV, named return_train_score: bool, default=False. As in scikit-learn, it controls if the cv_results_ should have the training scores.

Docs:

  • Edited all demos to be in the jupyter notebook format.
  • Added embedded jupyter notebooks examples in read the docs page.
  • The modules of the package now have a summary of their classes/functions in the docs.
  • Updated the callbacks and custom callbacks tutorials to add a new TensorBoard callback and the new methods on the base callback.

Internal:

  • Now the HallofFame (hof) uses the self.best_params_ for the position 0, to be consistent with the
    scikit-learn API and parameters like self.best_index_
  • MLflow now has unit tests by @Turtle24

Thanks to new contributors for helping in this project! @Raul9595 @Turtle24

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