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SimonBlanke / Gradient-Free-Optimizers

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
https://simonblanke.github.io/gradient-free-optimizers-documentation
MIT License
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cleanup warm_start_smbo data #9

Closed SimonBlanke closed 3 years ago

SimonBlanke commented 3 years ago

In the method "init_warm_start_smbo" in smbo.py the pandas dataframe is separated into X_sample (parameter) and Y_sample (score). The dataframe should be cleaned from all data that is not within the current search space before passing it. This will improve the optimization time and avoid predicted new positions outside the current search space.