def test_sobol_GPEI(self):
"""Tests sobol + GPEI instantiation."""
exp = get_branin_experiment()
# Check that factory generates a valid sobol modelbridge.
sobol = get_sobol(search_space=exp.search_space)
self.assertIsInstance(sobol, RandomModelBridge)
for _ in range(5):
sobol_run = sobol.gen(n=1)
exp.new_batch_trial().add_generator_run(sobol_run).run()
# Check that factory generates a valid GP+EI modelbridge.
exp.optimization_config = get_branin_optimization_config()
gpei = get_GPEI(experiment=exp, data=exp.fetch_data())
self.assertIsInstance(gpei, TorchModelBridge)
gpei = get_GPEI(experiment=exp,
data=exp.fetch_data(),
search_space=exp.search_space)
self.assertIsInstance(gpei, TorchModelBridge)
botorch = get_botorch(experiment=exp, data=exp.fetch_data())
self.assertIsInstance(botorch, TorchModelBridge)
def run_sparse(self):
sparse_exp = SparseExperiment(self.epochs1, **self.__dict__)
self.exp, self.data = sparse_exp.create_load_experiment()
sobol = get_sobol(self.exp.search_space)
sobol = self.run_model(self.r1, sobol, model_type="random")
self.exp.optimization_config.objective.metrics[0].epochs = self.epochs2
if self.arc:
botorch = get_botorch_arc(experiment=self.exp, data=self.data)
else:
botorch = get_botorch(experiment=self.exp, data=self.data)
botorch = self.run_model(self.r2, botorch, model_type="bo")
if self.morphisms:
self.pareto_arms = clean_models_return_pareto(
self.data, self.models_path)
self.develop_morphisms(botorch)
# #### Run the optimization loop
#
# We're ready to run the Bayesian Optimization loop.
for i in range(5):
print(f"Running optimization batch {i+1}/5...")
# user added:
# get_botorch is a self-contained macro-framework that does everything for you:
# 1) It fits the GP model to the new data
# 2) If optimizes the acquisition function and provides the next candidate
# 3) It evaluates such candidate (next point)
# 4) It returns the best suggestion (best guess)
model = get_botorch(
experiment=exp,
data=exp.eval(),
search_space=exp.search_space,
model_constructor=_get_and_fit_simple_custom_gp,
)
batch = exp.new_trial(generator_run=model.gen(1))
# user TODO: See https://ax.dev/api/_modules/ax/modelbridge/factory.html#get_botorch
# 1) Pass a custom acquisition function constructor
# 2) Maybe change the optimizer
# 3)
print(batch)
print("Done!")