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CHANGELOG.md

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Version 0.7.2

New features

  • Add expected_minimum_random_sampling
  • New plot examples
  • Add more parameter to plot_objective
  • Return ordereddict in point_asdict
  • update_next() and get_results() added to Optimize

Bug fixes

  • Fix searchcv rank (issue #831)
  • Fix random forest regressor (issue #766)
  • Fix doc examples
  • Fix integer normalize by using round()
  • Fix random forest regressor (Add missing min_impurity_decrease)

Maintenance

  • Fix license detection in github
  • Add doctest to CI

Version 0.7.1

New features

  • Sphinx documentation
  • notebooks are replaced by sphinx-gallery
  • New StringEncoder, can be used in Categoricals
  • Remove string conversion in Identity
  • dtype can be set in Integer and Real

Bug fixes

  • Fix categorical space (issue #821)
  • int can be set as dtype to fix issue #790

Maintenance

  • Old pdoc scripts are removed and replaced by sphinx

Version 0.7

New features

  • Models queue has now a customizable size (model_queue_size).
  • Add log-uniform prior to Integer space
  • Support for plotting categorical dimensions

Bug fixes

  • Allow BayesSearchCV to work with sklearn 0.21
  • Reduce the amount of deprecation warnings in unit tests

Maintenance

  • joblib instead of sklearn.externals.joblib
  • Improve travis CI unit tests (Different sklearn version are checked)
  • Added versioneer support, to keep things simple and to fix pypi deploy

Version 0.6

Highly composite six.

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

Version 0.5.2

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]')

Version 0.5

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.

Version 0.4

Go forth!

New features

  • Support early stopping of optimization loop.
  • Benchmarking scripts to evaluate performance of different surrogate models.
  • Support for parallel evaluations of the objective function via several constant liar stategies.
  • BayesSearchCV as a drop in replacement for scikit-learn's GridSearchCV.
  • New acquisition functions "EIps" and "PIps" that takes into account function compute time.

Bug fixes

  • Fixed inference of dimensions of type Real.

API changes

  • Change interface of GradientBoostingQuantileRegressor's predict method to match return type of other regressors
  • Dimensions of type Real are now inclusive of upper bound.

Version 0.3

Third time's a charm.

New features

  • Accuracy improvements of the optimization of the acquisition function by pre-selecting good candidates as starting points when using acq_optimizer='lbfgs'.
  • Support a ask-and-tell interface. Check out the Optimizer class if you need fine grained control over the iterations.
  • Parallelize L-BFGS minimization runs over the acquisition function.
  • Implement weighted hamming distance kernel for problems with only categorical dimensions.
  • New acquisition function gp_hedge that probabilistically chooses one of EI, PI or LCB at every iteration depending upon the cumulative gain.

Bug fixes

  • Warnings are now raised if a point is chosen as the candidate optimum multiple times.
  • Infinite gradients that were raised in the kernel gradient computation are now fixed.
  • Integer dimensions are now normalized to [0, 1] internally in gp_minimize.

API Changes.

  • The default acq_optimizer function has changed from "auto" to "lbfgs" in gp_minimize.

Version 0.2

New features

  • Speed improvements when using gp_minimize with acq_optimizer='lbfgs' and acq_optimizer='auto' when all the search-space dimensions are Real.
  • Persistence of minimization results using skopt.dump and skopt.load.
  • Support for using arbitrary estimators that implement a return_std argument in their predict method by means of base_minimize from skopt.optimizer.
  • Support for tuning noise in gp_minimize using the noise argument.
  • TimerCallback in skopt.callbacks to log the time between iterations of the minimization loop.

Version 0.1

First light!

New features

  • Bayesian optimization via gp_minimize.
  • Tree-based sequential model-based optimization via forest_minimize and gbrt_minimize, with support for multi-threading.
  • Support of LCB, EI and PI as acquisition functions.
  • Plotting functions for inspecting convergence, evaluations and the objective function.
  • API for specifying and sampling from a parameter space.

Contributors

See AUTHORS.md.