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Add Optimizer min_iteration_per_job for all SearchStrategies

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This commit is contained in:
allegroai 2020-08-08 12:39:56 +03:00
parent ef83a648eb
commit 35fa4f010a
4 changed files with 23 additions and 10 deletions
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4
examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py
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@ -94,8 +94,8 @@ an_optimizer = HyperParameterOptimizer(
# If OptimizerBOHB is used, it defined the maximum budget in terms of full jobs
# basically the cumulative number of iterations will not exceed total_max_jobs * max_iteration_per_job
total_max_jobs=10,
# This is only applicable for OptimizerBOHB and ignore by the rest
# set the minimum number of iterations for an experiment, before early stopping
# set the minimum number of iterations for an experiment, before early stopping.
# Does not apply for simple strategies such as RandomSearch or GridSearch
min_iteration_per_job=10,
# Set the maximum number of iterations for an experiment to execute
# (This is optional, unless using OptimizerBOHB where this is a must)

3
trains/automation/hpbandster/bandster.py
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@ -178,7 +178,8 @@ class OptimizerBOHB(SearchStrategy, RandomSeed):
base_task_id=base_task_id, hyper_parameters=hyper_parameters, objective_metric=objective_metric,
execution_queue=execution_queue, num_concurrent_workers=num_concurrent_workers,
pool_period_min=pool_period_min, time_limit_per_job=time_limit_per_job,
max_iteration_per_job=max_iteration_per_job, total_max_jobs=total_max_jobs)
max_iteration_per_job=max_iteration_per_job, min_iteration_per_job=min_iteration_per_job,
total_max_jobs=total_max_jobs)
self._max_iteration_per_job = max_iteration_per_job
self._min_iteration_per_job = min_iteration_per_job
verified_bohb_kwargs = ['eta', 'min_budget', 'max_budget', 'min_points_in_model', 'top_n_percent',

7
trains/automation/optimization.py
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@ -239,6 +239,7 @@ class SearchStrategy(object):
num_concurrent_workers, # type: int
pool_period_min=2., # type: float
time_limit_per_job=None, # type: Optional[float]
min_iteration_per_job=None, # type: Optional[int]
max_iteration_per_job=None, # type: Optional[int]
total_max_jobs=None, # type: Optional[int]
**_ # type: Any
@ -255,8 +256,9 @@ class SearchStrategy(object):
:param float pool_period_min: The time between two consecutive pools (minutes).
:param float time_limit_per_job: The maximum execution time per single job in minutes. When time limit is
exceeded, the job is aborted. (Optional)
:param int max_iteration_per_job: The maximum iterations (of the Objective metric) per single job. When maximum
iterations is exceeded, the job is aborted. (Optional)
:param int min_iteration_per_job: The minimum iterations (of the Objective metric) per single job (Optional)
:param int max_iteration_per_job: The maximum iterations (of the Objective metric) per single job.
When maximum iterations is exceeded, the job is aborted. (Optional)
:param int total_max_jobs: The total maximum jobs for the optimization process. The default value is ``None``,
for unlimited.
"""
@ -269,6 +271,7 @@ class SearchStrategy(object):
self.pool_period_minutes = pool_period_min
self.time_limit_per_job = time_limit_per_job
self.max_iteration_per_job = max_iteration_per_job
self.min_iteration_per_job = min_iteration_per_job
self.total_max_jobs = total_max_jobs
self._stop_event = Event()
self._current_jobs = []

19
trains/automation/optuna/optuna.py
View File

@ -15,13 +15,15 @@ except ImportError:
class OptunaObjective(object):
def __init__(self, base_task_id, queue_name, optimizer, max_iteration_per_job, sleep_interval, config_space):
# type: (str, str, OptimizerOptuna, int, float, dict) -> None
def __init__(self, base_task_id, queue_name, optimizer, max_iteration_per_job, min_iteration_per_job,
sleep_interval, config_space):
# type: (str, str, OptimizerOptuna, int, Optional[int], float, dict) -> None
self.base_task_id = base_task_id
self.optimizer = optimizer
self.queue_name = queue_name
self.sleep_interval = sleep_interval
self.max_iteration_per_job = max_iteration_per_job
self.min_iteration_per_job = min_iteration_per_job
self._config_space = config_space
def objective(self, trial):
@ -57,7 +59,9 @@ class OptunaObjective(object):
trial.report(value=iteration_value[1], step=iteration_value[0])
# Handle pruning based on the intermediate value.
if trial.should_prune():
if trial.should_prune() and (
not self.min_iteration_per_job or
iteration_value[0] >= self.min_iteration_per_job):
current_job.abort()
raise optuna.TrialPruned()
@ -87,6 +91,7 @@ class OptimizerOptuna(SearchStrategy):
max_iteration_per_job, # type: Optional[int]
total_max_jobs, # type: Optional[int]
pool_period_min=2., # type: float
min_iteration_per_job=None, # type: Optional[int]
time_limit_per_job=None, # type: Optional[float]
optuna_sampler=None, # type: Optional[optuna.samplers.base]
optuna_pruner=None, # type: Optional[optuna.pruners.base]
@ -97,7 +102,7 @@ class OptimizerOptuna(SearchStrategy):
"""
Initialize am Optuna search strategy optimizer
Optuna performs robust and efficient hyperparameter optimization at scale by combining.
Specific hyper-parameter pruning strategy can be selected via `sampler` and `pruner` argyments
Specific hyper-parameter pruning strategy can be selected via `sampler` and `pruner` arguments
:param str base_task_id: Task ID (str)
:param list hyper_parameters: list of Parameter objects to optimize over
@ -114,6 +119,8 @@ class OptimizerOptuna(SearchStrategy):
This means more than total_max_jobs could be created, as long as the cumulative iterations
(summed over all created jobs) will not exceed `max_iteration_per_job * total_max_jobs`
:param float pool_period_min: time in minutes between two consecutive pools
:param int min_iteration_per_job: The minimum number of iterations (of the Objective metric) per single job,
before early stopping the Job. (Optional)
:param float time_limit_per_job: Optional, maximum execution time per single job in minutes,
when time limit is exceeded job is aborted
:param optuna_kwargs: arguments passed directly to the Optuna object
@ -122,7 +129,8 @@ class OptimizerOptuna(SearchStrategy):
base_task_id=base_task_id, hyper_parameters=hyper_parameters, objective_metric=objective_metric,
execution_queue=execution_queue, num_concurrent_workers=num_concurrent_workers,
pool_period_min=pool_period_min, time_limit_per_job=time_limit_per_job,
max_iteration_per_job=max_iteration_per_job, total_max_jobs=total_max_jobs)
max_iteration_per_job=max_iteration_per_job, min_iteration_per_job=min_iteration_per_job,
total_max_jobs=total_max_jobs)
self._optuna_sampler = optuna_sampler
self._optuna_pruner = optuna_pruner
verified_optuna_kwargs = []
@ -154,6 +162,7 @@ class OptimizerOptuna(SearchStrategy):
queue_name=self._execution_queue,
optimizer=self,
max_iteration_per_job=self.max_iteration_per_job,
min_iteration_per_job=self.min_iteration_per_job,
sleep_interval=int(self.pool_period_minutes * 60),
config_space=config_space,
)