deephyper.search.hps.AMBS#

class deephyper.search.hps.AMBS(problem, evaluator, random_state: int = None, log_dir: str = '.', verbose: int = 0, surrogate_model='RF', acq_func: str = 'UCB', acq_optimizer: str = 'auto', kappa: float = 1.96, xi: float = 0.001, n_points: int = 10000, filter_duplicated: bool = True, update_prior: bool = False, multi_point_strategy: str = 'cl_max', n_jobs: int = 1, n_initial_points=10, initial_points=None, sync_communication: bool = False, filter_failures: str = 'mean', **kwargs)[source]#

Bases: CBO

AMBS is now deprecated and will be removed in the future use ‘CBO’ (Centralized Bayesian Optimization) instead!

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.

fit_generative_model

Learn the distribution of hyperparameters for the top-(1-q)x100% configurations and sample from this distribution.

fit_search_space

Apply prior-guided transfer learning based on a DataFrame of results.

fit_surrogate

Fit the surrogate model of the search from a checkpointed Dataframe.

search

Execute the search algorithm.

to_json

Returns a json version of the search object.

Attributes

search_id

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 is True if the point is Pareto efficient.

Parameters:

df (pd.DataFrame) – the input results DataFrame.

fit_generative_model(df, q=0.9, n_iter_optimize=0, n_samples=100)#

Learn the distribution of hyperparameters for the top-(1-q)x100% configurations and sample from this distribution. It can be used for transfer learning. For multiobjective problems, this function computes the top-(1-q)x100% configurations in terms of their ranking with respect to pareto efficiency: all points on the first non-dominated pareto front have rank 1 and in general, points on the k’th non-dominated front have rank k.

Example Usage:

>>> search = CBO(problem, evaluator)
>>> search.fit_surrogate("results.csv")
Parameters:

  • df (str|DataFrame) – a dataframe or path to CSV from a previous search.

  • q (float, optional) – the quantile defined the set of top configurations used to bias the search. Defaults to 0.90 which select the top-10% configurations from df.

  • n_iter_optimize (int, optional) – the number of iterations used to optimize the generative model which samples the data for the search. Defaults to 0 with no optimization for the generative model.

  • n_samples (int, optional) – the number of samples used to score the generative model.

Returns:

score, model which are a metric which measures the quality of the learned generated-model and the generative model respectively.

Return type:

tuple

fit_search_space(df, fac_numerical=0.125, fac_categorical=10)#

Apply prior-guided transfer learning based on a DataFrame of results.

Example Usage:

>>> search = CBO(problem, evaluator)
>>> search.fit_surrogate("results.csv")
Parameters:

  • df (str|DataFrame) – a checkpoint from a previous search.

  • fac_numerical (float) – the factor used to compute the sigma of a truncated normal distribution based on sigma = max(1.0, (upper - lower) * fac_numerical). A small large factor increase exploration while a small factor increase exploitation around the best-configuration from the df parameter.

  • fac_categorical (float) – the weight given to a categorical feature part of the best configuration. A large weight > 1 increase exploitation while a small factor close to 1 increase exploration.

fit_surrogate(df)#

Fit the surrogate model of the search from a checkpointed Dataframe.

Parameters:

df (str|DataFrame) – a checkpoint from a previous search.

Example Usage:

>>> search = CBO(problem, evaluator)
>>> search.fit_surrogate("results.csv")
search(max_evals: int = -1, timeout: int = None)#

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.

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.