def load_experiment_from_database(self, experiment_name: str) -> None:
"""Load an existing experiment from database using the `DBSettings`
passed to this `AxClient` on instantiation.
Args:
experiment_name: Name of the experiment.
Returns:
Experiment object.
"""
if not self.db_settings:
raise ValueError( # pragma: no cover
"Cannot load an experiment in the absence of the DB settings."
"Please initialize `AxClient` with DBSettings.")
experiment, generation_strategy = load_experiment_and_generation_strategy(
experiment_name=experiment_name, db_settings=self.db_settings)
self._experiment = experiment
if generation_strategy is None: # pragma: no cover
self._generation_strategy = choose_generation_strategy(
search_space=self._experiment.search_space,
enforce_sequential_optimization=self.
_enforce_sequential_optimization,
random_seed=self._random_seed,
)
else:
self._generation_strategy = generation_strategy
def create_experiment(
self,
parameters: List[Dict[str, Union[TParamValue, List[TParamValue]]]],
name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
status_quo: Optional[TParameterization] = None,
) -> None:
"""Create a new experiment and save it if DBSettings available.
Args:
parameters: List of dictionaries representing parameters in the
experiment search space. Required elements in the dictionaries
are: "name" (name of this parameter, string), "type" (type of the
parameter: "range", "fixed", or "choice", string), and "bounds"
for range parameters (list of two values, lower bound first),
"values" for choice parameters (list of values), and "value" for
fixed parameters (single value).
objective: Name of the metric used as objective in this experiment.
This metric must be present in `raw_data` argument to `complete_trial`.
name: Name of the experiment to be created.
minimize: Whether this experiment represents a minimization problem.
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "x3 + x4 + x5 >= 2". For sum
constraints, any number of arguments is accepted, and acceptable
operators are "<=" and ">=".
outcome_constraints: List of string representation of outcome
constraints of form "metric_name >= bound", like "m1 <= 3."
status_quo: Parameterization of the current state of the system.
If set, this will be added to each trial to be evaluated alongside
test configurations.
"""
if self.db_settings and not name:
raise ValueError( # pragma: no cover
"Must give the experiment a name if `db_settings` is not None."
)
self._experiment = make_experiment(
name=name,
parameters=parameters,
objective_name=objective_name,
minimize=minimize,
parameter_constraints=parameter_constraints,
outcome_constraints=outcome_constraints,
status_quo=status_quo,
)
if self._generation_strategy is None:
self._generation_strategy = choose_generation_strategy(
search_space=self._experiment.search_space,
enforce_sequential_optimization=self.
_enforce_sequential_optimization,
random_seed=self._random_seed,
)
self._save_experiment_and_generation_strategy_to_db_if_possible()
def create_experiment(
self,
parameters: List[Dict[str, Union[TParamValue, List[TParamValue]]]],
name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
) -> None:
"""Create a new experiment and save it if DBSettings available.
Args:
parameters: List of dictionaries representing parameters in the
experiment search space. Required elements in the dictionaries
are: "name" (name of this parameter, string), "type" (type of the
parameter: "range", "fixed", or "choice", string), and "bounds"
for range parameters (list of two values, lower bound first),
"values" for choice parameters (list of values), and "value" for
fixed parameters (single value).
objective: Name of the metric used as objective in this experiment.
This metric must be present in `raw_data` argument to `log_data`.
name: Name of the experiment to be created.
minimize: Whether this experiment represents a minimization problem.
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "x3 + x4 >= 2".
outcome_constraints: List of string representation of outcome
constraints of form "metric_name >= bound", like "m1 <= 3."
"""
if self.db_settings and not name:
raise ValueError( # pragma: no cover
"Must give the experiment a name if `db_settings` is not None."
)
self._experiment = make_experiment(
name=name,
parameters=parameters,
objective_name=objective_name,
minimize=minimize,
parameter_constraints=parameter_constraints,
outcome_constraints=outcome_constraints,
)
if self.generation_strategy is None:
self.generation_strategy = choose_generation_strategy(
search_space=self._experiment.search_space,
enforce_sequential_optimization=self.
_enforce_sequential_optimization,
)
self._save_experiment_if_possible()
def __init__(
self,
experiment: Experiment,
total_trials: int = 20,
arms_per_trial: int = 1,
wait_time: int = 0,
run_async: bool = False, # TODO[Lena],
) -> None:
assert not run_async, "OptimizationLoop does not yet support async."
self.wait_time = wait_time
self.total_trials = total_trials
self.arms_per_trial = arms_per_trial
assert len(experiment.trials) == 0, (
"Optimization Loop should not be initialized with an experiment "
"that has trials already.")
self.experiment = experiment
self.generation_strategy = choose_generation_strategy(
search_space=experiment.search_space,
arms_per_trial=self.arms_per_trial)
self.current_trial = 0
def get_generation_strategy() -> GenerationStrategy:
return choose_generation_strategy(search_space=get_search_space())
def create_experiment(
self,
parameters: List[Dict[str, Union[TParamValue, List[TParamValue]]]],
name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
status_quo: Optional[TParameterization] = None,
overwrite_existing_experiment: bool = False,
experiment_type: Optional[str] = None,
) -> None:
"""Create a new experiment and save it if DBSettings available.
Args:
parameters: List of dictionaries representing parameters in the
experiment search space. Required elements in the dictionaries
are: "name" (name of this parameter, string), "type" (type of the
parameter: "range", "fixed", or "choice", string), and "bounds"
for range parameters (list of two values, lower bound first),
"values" for choice parameters (list of values), and "value" for
fixed parameters (single value).
objective: Name of the metric used as objective in this experiment.
This metric must be present in `raw_data` argument to `complete_trial`.
name: Name of the experiment to be created.
minimize: Whether this experiment represents a minimization problem.
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "x3 + x4 + x5 >= 2". For sum
constraints, any number of arguments is accepted, and acceptable
operators are "<=" and ">=".
outcome_constraints: List of string representation of outcome
constraints of form "metric_name >= bound", like "m1 <= 3."
status_quo: Parameterization of the current state of the system.
If set, this will be added to each trial to be evaluated alongside
test configurations.
overwrite_existing_experiment: If `DBSettings` were provided on
instantiation and the experiment being created has the same name
as some experiment already stored, whether to overwrite the
existing experiment. Defaults to False.
"""
if self.db_settings and not name:
raise ValueError( # pragma: no cover
"Must give the experiment a name if `db_settings` is not None."
)
if self.db_settings:
existing = None
try:
existing, _ = load_experiment_and_generation_strategy(
experiment_name=not_none(name), db_settings=self.db_settings
)
except ValueError: # Experiment does not exist, nothing to do.
pass
if existing and overwrite_existing_experiment:
logger.info(f"Overwriting existing experiment {name}.")
elif existing:
raise ValueError(
f"Experiment {name} exists; set the `overwrite_existing_"
"experiment` to `True` to overwrite with new experiment "
"or use `ax_client.load_experiment_from_database` to "
"continue an existing experiment."
)
self._experiment = make_experiment(
name=name,
parameters=parameters,
objective_name=objective_name,
minimize=minimize,
parameter_constraints=parameter_constraints,
outcome_constraints=outcome_constraints,
status_quo=status_quo,
experiment_type=experiment_type,
)
if self._generation_strategy is None:
self._generation_strategy = choose_generation_strategy(
search_space=self._experiment.search_space,
enforce_sequential_optimization=self._enforce_sequential_optimization,
random_seed=self._random_seed,
)
self._save_experiment_and_generation_strategy_to_db_if_possible()