deephyper.hpo.Search

class deephyper.hpo.Search(problem, random_state=None, log_dir: str = '.', verbose: int = 0, stopper: Stopper | None = None, checkpoint_history_to_csv: bool = True, solution_selection: Literal['argmax_obs', 'argmax_est'] | SolutionSelection | None = None, checkpoint_restart: bool = False)

Bases: ABC

Base class search/optimization algorithms.

Parameters:

• problem – object describing the search/optimization problem.

• random_state (np.random.RandomState, optional) – Initial random state of the search. Defaults to None.

• log_dir (str, optional) – Path to the directoy where results of the search are stored. Defaults to ".".

• verbose (int, optional) – Use verbose mode. Defaults to 0.

• stopper (Stopper, optional) – a stopper to leverage multi-fidelity when evaluating the function. Defaults to None which does not use any stopper.

• checkpoint_history_to_csv (bool) – wether the results from progressively collected evaluations should be checkpointed regularly to disc as a csv. Defaults to True.

• solution_selection (Literal["argmax_obs", "argmax_est"] | SolutionSelection, optional) – the solution selection strategy. It can be a string where "argmax_obs" would select the argmax of observed objective values, and "argmax_est" would select the argmax of estimated objective values (through a predictive model).

• checkpoint_restart (bool) –

  …

Methods

ask	Ask the search for new configurations to evaluate.
check_evaluator	Check if the input is a callable, an evaluator or else.
dump_jobs_done_to_csv	Dump jobs completed to CSV in log_dir.
get_params	Get parameters used for the search object.
reload_checkpoint
save_params	Save the search parameters to a JSON file in the log folder.
search	Execute the search algorithm.
tell	Tell the search the results of the evaluations.

Attributes

search_id

The identifier of the search used by the evaluator.

ask(n: int = 1) → List[Dict]

Ask the search for new configurations to evaluate.

Parameters:

n (int, optional) – The number of configurations to ask. Defaults to 1.

Returns:

a list of hyperparameter configurations to evaluate.

Return type:

List[Dict]

check_evaluator(evaluator)

Check if the input is a callable, an evaluator or else.

dump_jobs_done_to_csv(flush: bool = False)

Dump jobs completed to CSV in log_dir.

Parameters:

flush (bool, optional) – Force the dumping if set to True. Defaults to False.

get_params() → dict[str, Any]

Get parameters used for the search object.

Returns:

A dictionary of the search parameters.

save_params(filename: str = 'params.json')

Save the search parameters to a JSON file in the log folder.

Parameters:

filename – Name of JSON file where search parameters are saved. Default is params.json.

search(evaluator, max_evals: int = -1, timeout: int | float | None = None, max_evals_strict: bool = False) → DataFrame

Execute the search algorithm.

Parameters:

• evaluator – object describing the evaluation process.

• max_evals (int, optional) – The maximum number of evaluations of the run function to perform before stopping the search. Defaults to -1, will run indefinitely.

• timeout (int, optional) – The time budget (in seconds) of the search before stopping. Defaults to None, will not impose a time budget.

• max_evals_strict (bool, optional) – If True the search will not spawn more than max_evals jobs. Defaults to False.

Returns:

A pandas DataFrame containing the evaluations performed or None if the

search could not evaluate any configuration.

This DataFrame contains the following columns: - p:HYPERPARAMETER_NAME: for each hyperparameter of the problem. - objective: for single objective optimization. - objective_0, objective_1, …: for multi-objective optimization. - job_id: the identifier of the job. - job_status: the status of the job at the end of the search. - m:METADATA_NAME: for each metadata of the problem. Some metadata are always

present like m:timestamp_submit and m:timestamp_gather which are the timestamps of the submission and gathering of the job.

Return type:

pd.DataFrame

property search_id

The identifier of the search used by the evaluator.

tell(results: list[tuple[dict[str, str | int | float | None], str | int | float | tuple[str | int | float]]])

Tell the search the results of the evaluations.

Parameters:

results (list[tuple[dict[str, Optional[str | int | float]], str | int | float]]) – a dictionary containing the results of the evaluations.