Stochastic Configuration

StochasticConfiguration for multi-trial stochastic optimisation.

class MetricCombinationRule(*values)

Rule for combining a metric across multiple stochastic trials.

MIN = 'MIN'

MAX = 'MAX'

MEAN = 'MEAN'

PVALUE_MAX = 'PVALUE_MAX'

PVALUE_MIN = 'PVALUE_MIN'

class StochasticConfiguration(runs, p, disable_evaluator_caching)

Configures the solver to run multiple evaluations per candidate solution.

Parameters:

• runs (int) – Number of evaluations per candidate.

• p (float) – Confidence level used by the p-value-based combination rules.

• disable_evaluator_caching (bool) – When True, force fresh evaluation each run instead of using the evaluator’s cache.

metric_rules: dict[str, MetricCombinationRule]

add_metric_rule(key, metric_rule)

Register a per-metric MetricCombinationRule.

Return type:

StochasticConfiguration