def get_empirical_bayes_thompson(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
num_samples: int = 10000,
min_weight: Optional[float] = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates an empirical Bayes / Thompson sampling model."""
if data.df.empty: # pragma: no cover
raise ValueError(
"Empirical Bayes Thompson sampler requires non-empty data.")
logger.info(
"Factory functions (like `get_empirical_bayes_thompson`) will soon be "
"deprecated). Use the model registry instead (`Models.EMPIRICAL_BAYES"
"(...)`).")
return checked_cast(
DiscreteModelBridge,
Models.EMPIRICAL_BAYES_THOMPSON(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
num_samples=num_samples,
min_weight=min_weight,
uniform_weights=uniform_weights,
),
)
def test_enum_empirical_bayes_thompson(self):
"""Tests EB/TS instantiation through the Models enum."""
exp = get_factorial_experiment()
factorial = Models.FACTORIAL(exp.search_space)
self.assertIsInstance(factorial, DiscreteModelBridge)
factorial_run = factorial.gen(n=-1)
exp.new_batch_trial().add_generator_run(factorial_run).run().mark_completed()
data = exp.fetch_data()
eb_thompson = Models.EMPIRICAL_BAYES_THOMPSON(
experiment=exp, data=data, min_weight=0.0
)
self.assertIsInstance(eb_thompson, DiscreteModelBridge)
self.assertIsInstance(eb_thompson.model, EmpiricalBayesThompsonSampler)
thompson_run = eb_thompson.gen(n=5)
self.assertEqual(len(thompson_run.arms), 5)
def get_empirical_bayes_thompson(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
num_samples: int = 10000,
min_weight: Optional[float] = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates an empirical Bayes / Thompson sampling model."""
if data.df.empty: # pragma: no cover
raise ValueError("Empirical Bayes Thompson sampler requires non-empty data.")
return checked_cast(
DiscreteModelBridge,
Models.EMPIRICAL_BAYES_THOMPSON(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
num_samples=num_samples,
min_weight=min_weight,
uniform_weights=uniform_weights,
),
)