Probability Of Improvement
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+ gpjax.decision_making.utility_functions.probability_of_improvement
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+ ProbabilityOfImprovement
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+ dataclass
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+ Bases: AbstractSinglePointUtilityFunctionBuilder
An acquisition function which returns the probability of improvement +of the objective function over the best observed value.
+More precisely, given a predictive posterior distribution of the objective +function , the probability of improvement at a test point is defined as: +$$ +where is the minimiser of the posterior mean +at previously observed values (to handle noisy observations).
+The probability of improvement can be easily computed using the +cumulative distribution function of the standard normal distribution : +$$ +where and are the mean and standard deviation of the +predictive distribution of the objective function at .
+References
+[1] Kushner, H. J. (1964). +A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. +Journal of Basic Engineering, 86(1), 97-106.
+[2] Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). +Taking the human out of the loop: A review of Bayesian optimization. +Proceedings of the IEEE, 104(1), 148-175. doi: 10.1109/JPROC.2015.2494218
+ + + + +
+ check_objective_present(posteriors: Mapping[str, AbstractPosterior], datasets: Mapping[str, Dataset]) -> None
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+ Check that the objective posterior and dataset are present in the posteriors and +datasets.
+ + + +Parameters:
+Name | +Type | +Description | +Default | +
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posteriors |
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+ Mapping[str, AbstractPosterior]
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+ Dictionary of posteriors to be + |
+ + required + | +
datasets |
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+ Mapping[str, Dataset]
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+ Dictionary of datasets which may be used + |
+ + required + | +
Raises:
+Type | +Description | +
---|---|
+ ValueError
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+ If the objective posterior or dataset are not present in the + |
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+ __init__() -> None
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+ build_utility_function(posteriors: Mapping[str, ConjugatePosterior], datasets: Mapping[str, Dataset], key: KeyArray) -> SinglePointUtilityFunction
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+ Constructs the probability of improvement utility function +using the predictive posterior of the objective function.
+ + + +Parameters:
+Name | +Type | +Description | +Default | +
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posteriors |
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+ Mapping[str, AbstractPosterior]
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+
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+ Dictionary of posteriors to be + |
+ + required + | +
datasets |
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+ Mapping[str, Dataset]
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+ Dictionary of datasets which may be used + |
+ + required + | +
key |
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+ KeyArray
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+ JAX PRNG key used for random number generation. Since + |
+ + required + | +
Returns:
+Name | Type | +Description | +
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SinglePointUtilityFunction |
+ SinglePointUtilityFunction
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+ the probability of improvement utility function. + |
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