Comments (1)
Thank you for your suggestion Max! As of now, this might be problematic because that means that in the outputted pd.DataFrame, individual cells might hold data that cannot be stored in a pandas DataFrame cell, like numpy arrays.
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Related Issues (20)
- change this to f3dasm.design
- if a already existing column name is added, np.nan is still added to rows
- Optimization requires your domain to have names x0, x1, x2, etc.
- Selecting experiments from ExperimentData copies columns incorrectly
- Missing information about benchmark functions
- Adding documentation about using f3dasm and slurm resource schedulers
- this method relies on the backend being a pandas dataframe HOT 1
- Parallel data generation: Unexpected behavior of Numpy random generator seed
- Implement a new `x0_selection` strategy that samples new samples for the initial populatioin
- Add functionality to select all jobs that are finished
- Improving usability by implementing helper function to convert arbitrary functions to DataGenerators
- Documentation is both hosted by GitHub Pages and ReadTheDocs
- Add `ExperimentData.from_file` with `hydra` to documentation
- Bug: sampling with constant variable with float
- Implement method to overwrite current decision vector instead of storing all of the history for optimization
- Implement callback functionality to optimization HOT 1
- Evaluating experimental data with a function whose signature does not strictly match the input variables will raise an error HOT 2
- Sampling an int with a large bound value raises a MemoryError HOT 2
- Parallel data generation: controlling the number of parallel processes HOT 2
- pd.EmptyData error in concurrent access `from_file` and `store` method
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