physbo.search.score_multi module
- physbo.search.score_multi.EHVI(fmean, fstd, pareto)[source]
Calculate Expected Hyper-Volume Improvement (EHVI).
Reference: (Couckuyt et al., 2014) Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization
- Parameters:
fmean (numpy.ndarray) – mean of the predictive distribution
fstd (numpy.ndarray) – standard deviation of the predictive distribution
pareto (Pareto object) – Pareto object
- Returns:
score – score of the candidate
- Return type:
numpy.ndarray
- physbo.search.score_multi.HVPI(fmean, fstd, pareto)[source]
Calculate Hypervolume-based Probability of Improvement (HVPI).
Reference: (Couckuyt et al., 2014) Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization
- Parameters:
fmean (numpy.ndarray) – mean of the predictive distribution
fstd (numpy.ndarray) – standard deviation of the predictive distribution
pareto (Pareto object) – Pareto object
- Returns:
score – score of the candidate
- Return type:
numpy.ndarray
- physbo.search.score_multi.TS(predictor_list, training_list, test, alpha=1, reduced_candidate_num=None)[source]
Thompson Sampling for multi-objective optimization.
- Parameters:
predictor_list (list of Predictor) – List of predictors for each objective.
training_list (list of Variable) – List of training data for each objective.
test (Variable) – Test points.
alpha (float, optional) – Scaling parameter for sampling from posterior distribution. Default is 1.
reduced_candidate_num (int, optional) – Number of candidates to randomly select for Pareto front calculation. If None or less than number of test points, all points are used. Default is None.
- Returns:
score_res – Score array with 1 at the chosen index and 0 elsewhere.
- Return type:
numpy.ndarray