ray.tune.search.dragonfly.DragonflySearch#

class ray.tune.search.dragonfly.DragonflySearch(optimizer: Optional[str] = None, domain: Optional[str] = None, space: Optional[Union[Dict, List[Dict]]] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, evaluated_rewards: Optional[List] = None, random_state_seed: Optional[int] = None, **kwargs)[source]#

Bases: ray.tune.search.searcher.Searcher

Uses Dragonfly to optimize hyperparameters.

Dragonfly provides an array of tools to scale up Bayesian optimisation to expensive large scale problems, including high dimensional optimisation. parallel evaluations in synchronous or asynchronous settings, multi-fidelity optimisation (using cheap approximations to speed up the optimisation process), and multi-objective optimisation. For more info:

To use this search algorithm, install Dragonfly:

$ pip install dragonfly-opt

This interface requires using FunctionCallers and optimizers provided by Dragonfly.

This searcher will automatically filter out any NaN, inf or -inf results.

Parameters

  • optimizer – Optimizer provided from dragonfly. Choose an optimiser that extends BlackboxOptimiser. If this is a string, domain must be set and optimizer must be one of [random, bandit, genetic].

  • domain – Optional domain. Should only be set if you don’t pass an optimizer as the optimizer argument. Has to be one of [cartesian, euclidean].

  • space – Search space. Should only be set if you don’t pass an optimizer as the optimizer argument. Defines the search space and requires a domain to be set. Can be automatically converted from the param_space dict passed to tune.Tuner().

  • metric – The training result objective value attribute. If None but a mode was passed, the anonymous metric _metric will be used per default.

  • mode – One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute.

  • points_to_evaluate – Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations.

  • evaluated_rewards – If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate.

  • random_state_seed – Seed for reproducible results. Defaults to None. Please note that setting this to a value will change global random state for numpy on initalization and loading from checkpoint.

Tune automatically converts search spaces to Dragonfly’s format:

from ray import tune

config = {
    "LiNO3_vol": tune.uniform(0, 7),
    "Li2SO4_vol": tune.uniform(0, 7),
    "NaClO4_vol": tune.uniform(0, 7)
}

df_search = DragonflySearch(
    optimizer="bandit",
    domain="euclidean",
    metric="objective",
    mode="max")

tuner = tune.Tuner(
    my_func,
    tune_config=tune.TuneConfig(
        search_alg=df_search
    ),
    param_space=config
)
tuner.fit()

If you would like to pass the search space/optimizer manually, the code would look like this:

from ray import tune

space = [{
    "name": "LiNO3_vol",
    "type": "float",
    "min": 0,
    "max": 7
}, {
    "name": "Li2SO4_vol",
    "type": "float",
    "min": 0,
    "max": 7
}, {
    "name": "NaClO4_vol",
    "type": "float",
    "min": 0,
    "max": 7
}]

df_search = DragonflySearch(
    optimizer="bandit",
    domain="euclidean",
    space=space,
    metric="objective",
    mode="max")

tuner = tune.Tuner(
    my_func,
    tune_config=tune.TuneConfig(
        search_alg=df_search
    ),
)
tuner.fit()

Methods

add_evaluated_trials(trials_or_analysis, metric)

Pass results from trials that have been evaluated separately.

on_trial_complete(trial_id[, result, error])

Passes result to Dragonfly unless early terminated or errored.

on_trial_result(trial_id, result)

Optional notification for result during training.

restore_from_dir(checkpoint_dir)

Restores the state of a searcher from a given checkpoint_dir.

save_to_dir(checkpoint_dir[, session_str])

Automatically saves the given searcher to the checkpoint_dir.

set_max_concurrency(max_concurrent)

Set max concurrent trials this searcher can run.

Attributes

CKPT_FILE_TMPL

FINISHED

metric

The training result objective value attribute.

mode

Specifies if minimizing or maximizing the metric.