Merged darnr's scikit-optimize fork into ProcessOptimizer. Here is their changelog:
New features
- `plot_regret` function for plotting the cumulative regret;
The purpose of such plot is to access how much an optimizer
is effective at picking good points.
- `CheckpointSaver` that can be used to save a
checkpoint after each iteration with skopt.dump
- `Space.from_yaml()`
to allow for external file to define Space parameters
Bug fixes
- Fixed numpy broadcasting issues in gaussian_ei, gaussian_pi
- Fixed build with newest scikit-learn
- Use native python types inside BayesSearchCV
- Include fit_params in BayesSearchCV refit
Maintenance
- Added `versioneer` support, to reduce changes with new version of the `skopt`
Bug fixes
- Separated `n_points` from `n_jobs` in `BayesSearchCV`.
- Dimensions now support boolean np.arrays.
Maintenance
- `matplotlib` is now an optional requirement (install with `pip install 'scikit-optimize[plots]'`)
High five!
New features
- Single element dimension definition, which can be used to
fix the value of a dimension during optimization.
- `total_iterations` property of `BayesSearchCV` that
counts total iterations needed to explore all subspaces.
- Add iteration event handler for `BayesSearchCV`, useful
for early stopping inside `BayesSearchCV` search loop.
- added `utils.use_named_args` decorator to help with unpacking named dimensions
when calling an objective function.
Bug fixes
- Removed redundant estimator fitting inside `BayesSearchCV`.
- Fixed the log10 transform for Real dimensions that would lead to values being
out of bounds.