deephyper.search.Search#

class deephyper.search.Search(problem, evaluator, random_state=None, log_dir='.', verbose=0, **kwargs)[source]#

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.

Methods

`check_evaluator`
`dump_context`	Dumps the context in the log folder.
`extend_results_with_pareto_efficient`	Extend the results DataFrame with a column `pareto_efficient` which is `True` if the point is Pareto efficient.
`search`	Execute the search algorithm.
`to_json`	Returns a json version of the search object.

Attributes

The identifier of the search used by the evaluator.

extend_results_with_pareto_efficient(df_path: str)[source]#

Extend the results DataFrame with a column pareto_efficient which is True if the point is Pareto efficient.

search(max_evals: int = -1, timeout: int = None, max_evals_strict: bool = False)[source]#

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.

Return type:

DataFrame

deephyper.search.Search