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modAL 0.3.0

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@cosmic-cortex cosmic-cortex released this 25 Apr 08:59
· 318 commits to master since this release
0.3.0
42f6a10

Release notes

New features

  • Bayesian optimization. Bayesian optimization is a method for optimizing black box functions for which evaluation may be expensive and derivatives may not be available. It uses a query loop very similar to active learning, which makes it possible to implement it using an API identical to the ActiveLearner. Sampling for values are made by strategies estimating the possible gains for each point. Among these, three strategies are implemented currently: probability of improvement, expected improvement and upper confidence bounds.

Changes

  • modAL.models.BaseLearner abstract base class implemented. ActiveLearner and BayesianOptimizer both inherit from it.
  • modAL.models.ActiveLearner.query() now passes the ActiveLearner object to the query function instead of just the estimator.

Fixes

  • modAL.utils.selection.multi_argmax() now works for arrays with shape (-1, ) as well as (-1, 1).
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