emukit.bayesian_optimization package

Subpackages

• emukit.bayesian_optimization.acquisitions package
  • Submodules
    • EntropySearch
      • EntropySearch.update_parameters()
      • EntropySearch.update_pmin()
      • EntropySearch.evaluate()
      • EntropySearch.has_gradients
    • MultiInformationSourceEntropySearch
    • ExpectedImprovement
      • ExpectedImprovement.evaluate()
      • ExpectedImprovement.evaluate_with_gradients()
      • ExpectedImprovement.has_gradients
    • MeanPluginExpectedImprovement
    • get_standard_normal_pdf_cdf()
    • MultipointExpectedImprovement
      • MultipointExpectedImprovement.evaluate()
      • MultipointExpectedImprovement.evaluate_with_gradients()
    • get_covariance_given_smallest()
    • get_covariance_given_value_of_i()
    • get_correlations_given_value_of_i()
    • decompose_mvn()
    • Phi_gradient()
    • Phi_hessian()
    • LocalPenalization
      • LocalPenalization.has_gradients
      • LocalPenalization.update_batches()
      • LocalPenalization.evaluate()
      • LocalPenalization.evaluate_with_gradients()
    • LogAcquisition
      • LogAcquisition.evaluate()
      • LogAcquisition.evaluate_with_gradients()
      • LogAcquisition.has_gradients
    • MaxValueEntropySearch
      • MaxValueEntropySearch.update_parameters()
      • MaxValueEntropySearch.evaluate()
      • MaxValueEntropySearch.has_gradients
    • MUMBO
      • MUMBO.update_parameters()
      • MUMBO.evaluate()
    • NegativeLowerConfidenceBound
      • NegativeLowerConfidenceBound.evaluate()
      • NegativeLowerConfidenceBound.evaluate_with_gradients()
      • NegativeLowerConfidenceBound.has_gradients
    • ProbabilityOfFeasibility
      • ProbabilityOfFeasibility.evaluate()
      • ProbabilityOfFeasibility.evaluate_with_gradients()
      • ProbabilityOfFeasibility.has_gradients
    • ProbabilityOfImprovement
      • ProbabilityOfImprovement.evaluate()
      • ProbabilityOfImprovement.evaluate_with_gradients()
      • ProbabilityOfImprovement.has_gradients
  • Module contents
• emukit.bayesian_optimization.interfaces package
  • Submodules
    • IEntropySearchModel
      • IEntropySearchModel.predict_covariance()
      • IEntropySearchModel.get_covariance_between_points()
  • Module contents
• emukit.bayesian_optimization.loops package
  • Submodules
    • BayesianOptimizationLoop
      • BayesianOptimizationLoop.get_results()
    • BayesianOptimizationResults
    • CostSensitiveBayesianOptimizationLoop
    • UnknownConstraintBayesianOptimizationLoop
  • Module contents

Submodules

emukit.bayesian_optimization.epmgp.joint_min(mu, var, with_derivatives=False)

Computes the probability of every given point to be the minimum based on the EPMGP[1] algorithm. [1] J. Cunningham, P. Hennig, and S. Lacoste-Julien. Gaussian probabilities and expectation propagation. under review. Preprint at arXiv, November 2011.

Parameters:

• mu (ndarray) – Mean value of each of the N points, dims (N,).

• var (ndarray) – Covariance matrix for all points, dims (N, N).

• with_derivatives (bool) – If True than also the gradients are computed.

Return type:

ndarray

Returns:

pmin distribution, dims (N,1).

emukit.bayesian_optimization.epmgp.min_factor(Mu, Sigma, k, gamma=1)

emukit.bayesian_optimization.epmgp.lt_factor(s, l, M, V, mp, p, gamma)

emukit.bayesian_optimization.epmgp.log_relative_gauss(z)

class emukit.bayesian_optimization.local_penalization_calculator.LocalPenalizationPointCalculator(acquisition, acquisition_optimizer, model, parameter_space, batch_size, fixed_lipschitz_constant=None, fixed_minimum=None)

Bases: CandidatePointCalculator

Candidate point calculator that computes a batch using local penalization from:

Batch Bayesian Optimization via Local Penalization. Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

compute_next_points(loop_state, context=None)

Computes a batch of points using local penalization.

Parameters:

• loop_state (LoopState) – Object containing the current state of the loop

• context (Optional[dict]) – Contains variables to fix through optimization of acquisition function. The dictionary key is the parameter name and the value is the value to fix the parameter to.

Return type:

ndarray

Module contents