def _run_GS_for_N_rounds(self, gs: GenerationStrategy, exp: Experiment,
num_rounds: int) -> List[int]:
could_gen = []
for _ in range(num_rounds):
(
num_trials_to_gen,
opt_complete,
) = gs.current_generator_run_limit()
self.assertFalse(opt_complete)
could_gen.append(num_trials_to_gen)
trials = []
for _ in range(num_trials_to_gen):
gr = gs.gen(
experiment=exp,
pending_observations=get_pending(experiment=exp),
)
trials.append(
exp.new_trial(gr).mark_running(no_runner_required=True))
for trial in trials:
exp.attach_data(get_branin_data(trial_indices=[trial.index]))
trial.mark_completed()
return could_gen
def testEmptyMetrics(self):
empty_experiment = Experiment(
name="test_experiment",
search_space=get_search_space(),
default_data_type=DataType.MAP_DATA,
)
self.assertEqual(empty_experiment.num_trials, 0)
empty_experiment.add_tracking_metric(Metric(name="ax_test_metric"))
self.assertTrue(empty_experiment.fetch_data().df.empty)
empty_experiment.attach_data(get_map_data())
def testEmptyMetrics(self):
empty_experiment = Experiment(name="test_experiment",
search_space=get_search_space())
self.assertEqual(empty_experiment.num_trials, 0)
with self.assertRaises(ValueError):
empty_experiment.fetch_data()
batch = empty_experiment.new_batch_trial()
self.assertEqual(empty_experiment.num_trials, 1)
with self.assertRaises(ValueError):
batch.fetch_data()
empty_experiment.add_tracking_metric(Metric(name="some_metric"))
empty_experiment.attach_data(get_data())
self.assertFalse(empty_experiment.fetch_data().df.empty)
def _benchmark_replication_Dev_API(
problem: BenchmarkProblem,
method: GenerationStrategy,
num_trials: int,
experiment_name: str,
batch_size: int = 1,
raise_all_exceptions: bool = False,
benchmark_trial: FunctionType = benchmark_trial,
verbose_logging: bool = True,
# Number of trials that need to fail for a replication to be considered failed.
failed_trials_tolerated: int = 5,
async_benchmark_options: Optional[AsyncBenchmarkOptions] = None,
) -> Tuple[Experiment, List[Exception]]:
"""Run a benchmark replication via the Developer API because the problem was
set up with Ax classes (likely to allow for additional complexity like
adding constraints or non-range parameters).
"""
if async_benchmark_options is not None:
raise NonRetryableBenchmarkingError(
"`async_benchmark_options` not supported when using the Dev API."
)
exceptions = []
experiment = Experiment(
name=experiment_name,
search_space=problem.search_space,
optimization_config=problem.optimization_config,
runner=SyntheticRunner(),
)
for trial_index in range(num_trials):
try:
gr = method.gen(experiment=experiment, n=batch_size)
if batch_size == 1:
trial = experiment.new_trial(generator_run=gr)
else:
assert batch_size > 1
trial = experiment.new_batch_trial(generator_run=gr)
trial.run()
# TODO[T94059549]: Rm 3 lines below when attaching data in fetch is fixed.
data = benchmark_trial(experiment=experiment, trial_index=trial_index)
if not data.df.empty:
experiment.attach_data(data=data)
except Exception as err: # TODO[T53975770]: test
if raise_all_exceptions:
raise
exceptions.append(err)
if len(exceptions) > failed_trials_tolerated:
raise RuntimeError( # TODO[T53975770]: test
f"More than {failed_trials_tolerated} failed for {experiment_name}."
)
return experiment, exceptions
def get_branin_experiment(
has_optimization_config: bool = True,
with_batch: bool = False,
with_trial: bool = False,
with_status_quo: bool = False,
with_fidelity_parameter: bool = False,
with_choice_parameter: bool = False,
with_str_choice_param: bool = False,
search_space: Optional[SearchSpace] = None,
minimize: bool = False,
named: bool = True,
with_completed_trial: bool = False,
) -> Experiment:
search_space = search_space or get_branin_search_space(
with_fidelity_parameter=with_fidelity_parameter,
with_choice_parameter=with_choice_parameter,
with_str_choice_param=with_str_choice_param,
)
exp = Experiment(
name="branin_test_experiment" if named else None,
search_space=search_space,
optimization_config=get_branin_optimization_config(
minimize=minimize) if has_optimization_config else None,
runner=SyntheticRunner(),
is_test=True,
)
if with_status_quo:
exp.status_quo = Arm(parameters={"x1": 0.0, "x2": 0.0})
if with_batch:
sobol_generator = get_sobol(search_space=exp.search_space)
sobol_run = sobol_generator.gen(n=15)
exp.new_batch_trial(
optimize_for_power=with_status_quo).add_generator_run(sobol_run)
if with_trial or with_completed_trial:
sobol_generator = get_sobol(search_space=exp.search_space)
sobol_run = sobol_generator.gen(n=1)
trial = exp.new_trial(generator_run=sobol_run)
if with_completed_trial:
trial.mark_running(no_runner_required=True)
exp.attach_data(
get_branin_data(trials=[trial])) # Add data for one trial
trial.mark_completed()
return exp
def testEmptyMetrics(self):
empty_experiment = Experiment(name="test_experiment",
search_space=get_search_space())
self.assertEqual(empty_experiment.num_trials, 0)
with self.assertRaises(ValueError):
empty_experiment.fetch_data()
batch = empty_experiment.new_batch_trial()
batch.mark_running(no_runner_required=True)
self.assertEqual(empty_experiment.num_trials, 1)
with self.assertRaises(ValueError):
batch.fetch_data()
empty_experiment.add_tracking_metric(Metric(name="ax_test_metric"))
self.assertTrue(empty_experiment.fetch_data().df.empty)
empty_experiment.attach_data(get_data())
batch.mark_completed()
self.assertFalse(empty_experiment.fetch_data().df.empty)
