deephyper.hpo.Search

class deephyper.hpo.Search(problem, evaluator, random_state=None, log_dir='.', verbose=0, stopper=None, **kwargs)

Bases: ABC

Abstract class which represents a search algorithm.

Parameters:

• problem – object describing the search/optimization problem.

• evaluator – object describing the evaluation process.

• 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.

Methods

ask	Ask the search for new configurations to evaluate.
check_evaluator
dump_context	Dumps the context in the log folder.
dump_jobs_done_to_csv	Dump jobs completed to CSV in log_dir.
extend_results_with_pareto_efficient_indicator	Extend the results DataFrame with Pareto-Front.
search	Execute the search algorithm.
tell	Tell the search the results of the evaluations.
to_json	Returns a json version of the search object.

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]

dump_context()

Dumps the context in the log folder.

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.

extend_results_with_pareto_efficient_indicator()

Extend the results DataFrame with Pareto-Front.

A column pareto_efficient is added to the dataframe. It is True if the point is Pareto efficient.

search(max_evals: int = -1, timeout: int = None, max_evals_strict: bool = False)

Execute the search algorithm.

Parameters:

• 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:

DataFrame

property search_id

The identifier of the search used by the evaluator.

tell(results: List[HPOJob])

Tell the search the results of the evaluations.

Parameters:

• results (List[HPOJob]) – a list of HPOJobs from which hyperparameters and objectives can

• retrieved. (be)

to_json()

Returns a json version of the search object.