deephyper.search.nas.AgEBO#
- class deephyper.search.nas.AgEBO(problem, evaluator, random_state: int = None, log_dir: str = '.', verbose: int = 0, population_size: int = 100, sample_size: int = 10, n_initial_points: int = 10, initial_points=None, surrogate_model: str = 'RF', acq_func: str = 'UCB', kappa: float = 0.001, xi: float = 1e-06, n_points: int = 10000, liar_strategy: str = 'cl_max', n_jobs: int = 1, sync_communication: bool = False)[source]#
Bases:
RegularizedEvolution
Aging evolution with Bayesian Optimization.
This algorithm build on the Regularized Evolution. It cumulates Hyperparameter optimization with Bayesian optimisation and Neural architecture search with regularized evolution.
- 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
.n_initial_points (int, optional) – Number of collected objectives required before fitting the surrogate-model. Defaults to
10
.initial_points (List[Dict], optional) – A list of initial points to evaluate where each point is a dictionnary where keys are names of hyperparameters and values their corresponding choice. Defaults to
None
for them to be generated randomly from the search space.surrogate_model (str, optional) – Surrogate model used by the Bayesian optimization. Can be a value in
["RF", "ET", "GBRT", "DUMMY"]
. Defaults to"RF"
.acq_func (str, optional) – Acquisition function used by the Bayesian optimization. Can be a value in
["UCB", "EI", "PI", "gp_hedge"]
. Defaults to"UCB"
.kappa (float, optional) – Manage the exploration/exploitation tradeoff for the “UCB” acquisition function. Defaults to
0.001
for strong exploitation.xi (float, optional) – Manage the exploration/exploitation tradeoff of
"EI"
and"PI"
acquisition function. Defaults to0.000001
for strong exploitation.n_points (int, optional) – The number of configurations sampled from the search space to infer each batch of new evaluated configurations. Defaults to
10000
.liar_strategy (str, optional) – Definition of the constant value use for the Liar strategy. Can be a value in
["cl_min", "cl_mean", "cl_max"]
. Defaults to"cl_max"
.n_jobs (int, optional) – Number of parallel processes used to fit the surrogate model of the Bayesian optimization. A value of
-1
will use all available cores. Defaults to1
.sync_communcation (bool, optional) – Performs the search in a batch-synchronous manner. Defaults to
False
for asynchronous updates.
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.