def get_factorial_experiment(
has_optimization_config: bool = True,
with_batch: bool = False,
with_status_quo: bool = False,
) -> Experiment:
exp = Experiment(
name="factorial_test_experiment",
search_space=get_factorial_search_space(),
optimization_config=OptimizationConfig(
objective=Objective(metric=get_factorial_metric())
)
if has_optimization_config
else None,
runner=SyntheticRunner(),
is_test=True,
tracking_metrics=[get_factorial_metric("secondary_metric")],
)
if with_status_quo:
exp.status_quo = Arm(
parameters={
"factor1": "level11",
"factor2": "level21",
"factor3": "level31",
}
)
if with_batch:
factorial_generator = get_factorial(search_space=exp.search_space)
factorial_run = factorial_generator.gen(n=-1)
exp.new_batch_trial(optimize_for_power=with_status_quo).add_generator_run(
factorial_run
)
return exp
def test_factorial(self):
"""Tests factorial instantiation."""
exp = get_factorial_experiment()
factorial = get_factorial(exp.search_space)
self.assertIsInstance(factorial, DiscreteModelBridge)
factorial_run = factorial.gen(n=-1)
self.assertEqual(len(factorial_run.arms), 24)
def test_thompson(self):
"""Tests TS instantiation."""
exp = get_factorial_experiment()
factorial = get_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()
thompson = get_thompson(experiment=exp, data=data)
self.assertIsInstance(thompson.model, ThompsonSampler)
def test_empirical_bayes_thompson(self):
"""Tests EB/TS instantiation."""
exp = get_factorial_experiment()
factorial = get_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 = get_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)