Hyperactive

Latest version: v4.6.0

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4.5.0

- add early stopping feature to custom optimization strategies
- display additional outputs from objective-function in results in command-line
- add type hints to hyperactive-api
- add tests for new features
- add test for verbosity=False

4.4.0

- add new feature: "optimization strategies"
- redesign progress-bar

4.3

- add new features from [GFO](https://github.com/SimonBlanke/Gradient-Free-Optimizers)
- add Spiral Optimization
- add Lipschitz Optimizer
- add DIRECT Optimizer
- print the random seed for reproducibility

4.0.0

3.2.4

- Decouple number of runs from active processes (Thanks to [PartiallyTyped](https://github.com/PartiallyTyped)). This reduces memory load if number of jobs is huge
- New feature: The progress board enables the user to monitor the optimization progress during the run.
- Display trend of best score
- Plot parameters and score in parallel coordinates
- Generate filter file to define an upper and/or lower bound for all parameters and the score in the parallel coordinate plot
- List parameters of 5 best scores
- add Python 3.8 to tests
- add warnings of search space values does not contain lists
- improve stability of result-methods
- add tests for hyperactive-memory + search spaces

2.3.0

- add Tree-structured optimization algorithm (idea from Hyperopt)
- add Decision-tree optimization algorithm (idea from sklearn)
- enable new optimization parameters for bayes-opt:
- max_sample_size: maximum number of samples for the gaussian-process-reg to train on. Sampling done by random choice.
- skip_retrain: skips the retraining of the gaussian-process-reg sometimes during the optimization run. Basically returns multiple predictions for next output (which should be apart from another)

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