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