deephyper.search.nas.RegularizedEvolutionMixed#
- class deephyper.search.nas.RegularizedEvolutionMixed(problem, evaluator, random_state: int = None, log_dir: str = '.', verbose: int = 0, population_size: int = 100, sample_size: int = 10, **kwargs)[source]#
Bases:
RegularizedEvolution
Extention of the Regularized evolution neural architecture search to the case of joint hyperparameter and neural architecture search.
- Parameters:
problem (NaProblem) – Neural architecture search problem describing the search space to explore.
evaluator (Evaluator) – An
Evaluator
instance responsible of distributing the tasks.random_state (int, optional) – Random seed. Defaults to None.
log_dir (str, optional) – Log directory where search’s results are saved. Defaults to “.”.
verbose (int, optional) – Indicate the verbosity level of the search. Defaults to 0.
population_size (int, optional) – the number of individuals to keep in the population. Defaults to 100.
sample_size (int, optional) – the number of individuals that should participate in each tournament. Defaults to 10.
Methods
check_evaluator
Dumps the context in the log folder.
Extend the results DataFrame with a column
pareto_efficient
which isTrue
if the point is Pareto efficient.Execute the search algorithm.
Returns a json version of the search object.
Attributes
The identifier of the search used by the evaluator.
- dump_context()#
Dumps the context in the log folder.
- extend_results_with_pareto_efficient(df_path: str)#
Extend the results DataFrame with a column
pareto_efficient
which isTrue
if the point is Pareto efficient.- Parameters:
df (pd.DataFrame) – the input results DataFrame.
- search(max_evals: int = -1, timeout: int = None)#
Execute the search algorithm.
- Parameters:
- Returns:
a pandas DataFrame containing the evaluations performed or
None
if the search could not evaluate any configuration.- Return type:
DataFrame
- property search_id#
The identifier of the search used by the evaluator.
- to_json()#
Returns a json version of the search object.