def test_enum_factorial(self):
"""Tests factorial 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)
self.assertEqual(len(factorial_run.arms), 24)
def test_enum_thompson(self):
"""Tests 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()
thompson = Models.THOMPSON(experiment=exp, data=data)
self.assertIsInstance(thompson.model, ThompsonSampler)
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()
data = exp.fetch_data()
thompson = get_thompson(experiment=exp, data=data)
self.assertIsInstance(thompson.model, ThompsonSampler)
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 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()
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)
