Skip to content

Version 0.9.0
Compare
Choose a tag to compare
@mlindauer mlindauer released this 16 Nov 14:48
v0.9.0
7f15a59
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

Major changes

  • ADD multiple optional initial designs: LHC, Factorial Design, Sobol
  • ADD fmin interface know uses BORF facade (should perform much better on continuous, low-dimensional functions)
  • ADD Hydra (see "Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection" by Xu et al)
  • MAINT Not every second configuration is randomly drawn, but SMAC samples configurations randomly with a given probability (default: 0.5)
  • MAINT parsing of options

Interface changes

  • ADD two new interfaces to optimize low dimensional continuous functions (w/o instances, docs missing)
    • BORF facade: Initial design + Tuned RF
    • BOGP interface: Initial design + GP
  • ADD options to control acquisition function optimization
  • ADD option to transform function values (log, inverse w/ and w/o scaling)
  • ADD option to set initial design

Minor changes

  • ADD output of estimated cost of final incumbent
  • ADD explanation of "deterministic" option in documentation
  • ADD save configspace as json
  • ADD random forest with automated HPO (not activated by default)
  • ADD optional linear cooldown for interleaving random configurations (not active by default)
  • MAINT Maximal cutoff time of pynisher set to UINT16
  • MAINT make SMAC deterministic if function is deterministic, the budget is limited and the run objective is quality
  • MAINT SLS on acquisition function (plateau walks)
  • MAINT README
  • FIX abort-on-first-run-crash
  • FIX pSMAC input directory parsing
  • FIX fmin interface with more than 10 parameters
  • FIX no output directory if set to '' (empty string)
  • FIX use np.log instead of np.log10
  • FIX No longer use law of total variance for uncertainty prediction for RFs as EPM, but only variance over trees (no variance in trees)
  • FIX Marginalize over instances inside of each tree of the forest leads to better uncertainty estimates (motivated by the original SMAC implementation)
Assets 2
Loading