syne_tune.blackbox_repository.simulated_tabular_backend module

syne_tune.blackbox_repository.simulated_tabular_backend.make_surrogate(surrogate=None, surrogate_kwargs=None)

Creates surrogate model (scikit-learn estimater)

Parameters:

• surrogate (Optional[str]) – A model that is fitted to predict objectives given any configuration. Possible examples: “KNeighborsRegressor”, MLPRegressor”, “XGBRegressor”, which would enable using the corresponding scikit-learn estimator. The model is fit on top of pipeline that applies basic feature-processing to convert hyperparameters rows in X to vectors. The configuration_space hyperparameters types are used to deduce the types of columns in X (for instance, categorical hyperparameters are one-hot encoded).

• surrogate_kwargs (Optional[dict]) – Arguments for the scikit-learn estimator, for instance {"n_neighbors": 1} can be used if surrogate="KNeighborsRegressor" is chosen.

Returns:

Scikit-learn estimator representing surrogate model

class syne_tune.blackbox_repository.simulated_tabular_backend.BlackboxRepositoryBackend(blackbox_name, elapsed_time_attr, max_resource_attr=None, seed=None, support_checkpointing=True, dataset=None, surrogate=None, surrogate_kwargs=None, add_surrogate_kwargs=None, config_space_surrogate=None, **simulatorbackend_kwargs)

Bases: _BlackboxSimulatorBackend

Allows to simulate a blackbox from blackbox-repository, selected by blackbox_name. See examples/launch_simulated_benchmark.py for an example on how to use. If you want to add a new dataset, see the Adding a new dataset section of syne_tune/blackbox_repository/README.md.

In each result reported to the simulator backend, the value for key elapsed_time_attr must be the time since the start of the evaluation. For example, if resource (or fidelity) equates to epochs trained, this would be the time from start of training until the end of the epoch. If the blackbox contains this information in a column, elapsed_time_attr should be its key.

If this backend is used with pause-and-resume multi-fidelity scheduling, it needs to track at which resource level each trial is paused. Namely, once a trial is resumed, all results for resources smaller or equal to that level are ignored, which simulates the situation that training is resumed from a checkpoint. This feature relies on result to be passed to pause_trial(). If this is not done, the backend cannot know from which resource level to resume a trial, so it starts the trial from scratch (which is equivalent to no checkpointing). The same happens if support_checkpointing is False.

Note

If the blackbox maintains cumulative time (elapsed_time), this is different from what SimulatorBackend requires for elapsed_time_attr, if a pause-and-resume scheduler is used. Namely, the backend requires the time since the start of the last recent resume. This conversion is done here internally in _run_job_and_collect_results(), which is called for each resume. This means that the field elapsed_time_attr is not what is received from the blackbox table, but instead what the backend needs.

max_resource_attr plays the same role as in HyperbandScheduler. If given, it is the key in a configuration config for the maximum resource. This is used by schedulers which limit each evaluation by setting this argument (e.g., promotion-based Hyperband).

If seed is given, entries of the blackbox are queried for this seed. Otherwise, a seed is drawn at random for every trial, but the same seed is used for all _run_job_and_collect_results() calls for the same trial. This is important for pause and resume scheduling.

Parameters:

• blackbox_name (str) – Name of a blackbox, must have been registered in blackbox repository.

• elapsed_time_attr (str) – Name of the column containing cumulative time

• max_resource_attr (Optional[str]) – See above

• seed (Optional[int]) – If given, this seed is used for all trial evaluations. Otherwise, seed is sampled at random for each trial. Only relevant for blackboxes with multiple seeds

• support_checkpointing (bool) – If False, the simulation does not do checkpointing, so resumed trials are started from scratch. Defaults to True

• dataset (Optional[str]) – Selects different versions of the blackbox (typically, the same ML model has been trained on different datasets)

• surrogate (Optional[str]) – Optionally, a model that is fitted to predict objectives given any configuration. Examples: “KNeighborsRegressor”, “MLPRegressor”, “XGBRegressor”, which would enable using the corresponding scikit-learn estimator, see also make_surrogate(). The model is fit on top of pipeline that applies basic feature-processing to convert hyperparameter rows in X to vectors. The configuration_space hyperparameter types are used to deduce the types of columns in X (for instance, categorical hyperparameters are one-hot encoded).

• surrogate_kwargs (Optional[dict]) – Arguments for the scikit-learn estimator, for instance {"n_neighbors": 1} can be used if surrogate="KNeighborsRegressor" is chosen. If blackbox_name is a YAHPO blackbox, then surrogate_kwargs is passed as yahpo_kwargs to load_blackbox(). In this case, surrogate is ignored (YAHPO always uses surrogates).

• config_space_surrogate (Optional[dict]) – If surrogate is given, this is the configuration space for the surrogate blackbox. If not given, the space of the original blackbox is used. However, its numerical parameters have finite domains (categorical or ordinal), which is usually not what we want for a surrogate.

• simulatorbackend_kwargs – Additional arguments to parent SimulatorBackend

property blackbox: Blackbox

class syne_tune.blackbox_repository.simulated_tabular_backend.UserBlackboxBackend(blackbox, elapsed_time_attr, max_resource_attr=None, seed=None, support_checkpointing=True, **simulatorbackend_kwargs)

Bases: _BlackboxSimulatorBackend

Version of _BlackboxSimulatorBackend, where the blackbox is given as explicit Blackbox object. See examples/launch_simulated_benchmark.py for an example on how to use.

Additional arguments on top of parent _BlackboxSimulatorBackend:

Parameters:

blackbox (Blackbox) – Blackbox to be used for simulation

property blackbox: Blackbox