ax.service¶

Ax Client¶

Managed Loop¶

class ax.service.managed_loop.OptimizationLoop(experiment: Experiment, evaluation_function: Union[Callable[[Dict[str, Union[None, str, bool, float, int]]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]], Callable[[Dict[str, Union[None, str, bool, float, int]], Optional[float]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]]], total_trials: int = 20, arms_per_trial: int = 1, random_seed: Optional[int] = None, wait_time: int = 0, run_async: bool = False, generation_strategy: Optional[GenerationStrategy] = None)[source]¶

Bases: object

Managed optimization loop, in which Ax oversees deployment of trials and gathering data.

full_run() → OptimizationLoop[source]¶

Runs full optimization loop as defined in the provided optimization plan.

get_best_point() → Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]][source]¶

Obtains the best point encountered in the course of this optimization.

get_current_model() → Optional[ModelBridge][source]¶

Obtain the most recently used model in optimization.

run_trial() → None[source]¶

Run a single step of the optimization plan.

static with_evaluation_function(parameters: List[Dict[str, Union[None, str, bool, float, int, Sequence[Union[None, str, bool, float, int]], Dict[str, List[str]]]]], evaluation_function: Union[Callable[[Dict[str, Union[None, str, bool, float, int]]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]], Callable[[Dict[str, Union[None, str, bool, float, int]], Optional[float]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]]], experiment_name: Optional[str] = None, objective_name: Optional[str] = None, minimize: bool = False, parameter_constraints: Optional[List[str]] = None, outcome_constraints: Optional[List[str]] = None, total_trials: int = 20, arms_per_trial: int = 1, wait_time: int = 0, random_seed: Optional[int] = None, generation_strategy: Optional[GenerationStrategy] = None) → OptimizationLoop[source]¶

Constructs a synchronous OptimizationLoop using an evaluation function.

classmethod with_runners_and_metrics(parameters: List[Dict[str, Union[None, str, bool, float, int, Sequence[Union[None, str, bool, float, int]], Dict[str, List[str]]]]], path_to_runner: str, paths_to_metrics: List[str], experiment_name: Optional[str] = None, objective_name: Optional[str] = None, minimize: bool = False, parameter_constraints: Optional[List[str]] = None, outcome_constraints: Optional[List[str]] = None, total_trials: int = 20, arms_per_trial: int = 1, wait_time: int = 0, random_seed: Optional[int] = None) → OptimizationLoop[source]¶

Constructs an asynchronous OptimizationLoop using Ax runners and metrics.

ax.service.managed_loop.optimize(parameters: List[Dict[str, Union[None, str, bool, float, int, Sequence[Union[None, str, bool, float, int]], Dict[str, List[str]]]]], evaluation_function: Union[Callable[[Dict[str, Union[None, str, bool, float, int]]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]], Callable[[Dict[str, Union[None, str, bool, float, int]], Optional[float]], Union[Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]], int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]], List[Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]], List[Tuple[Dict[str, Hashable], Dict[str, Union[int, float, floating, integer, Tuple[Union[int, float, floating, integer], Optional[Union[int, float, floating, integer]]]]]]]]]], experiment_name: Optional[str] = None, objective_name: Optional[str] = None, minimize: bool = False, parameter_constraints: Optional[List[str]] = None, outcome_constraints: Optional[List[str]] = None, total_trials: int = 20, arms_per_trial: int = 1, random_seed: Optional[int] = None, generation_strategy: Optional[GenerationStrategy] = None) → Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]], Experiment, Optional[ModelBridge]][source]¶

Construct and run a full optimization loop.

Interactive Loop¶

Scheduler¶

class ax.service.utils.scheduler_options.SchedulerOptions(max_pending_trials: int = 10, trial_type: ~ax.service.utils.scheduler_options.TrialType = TrialType.TRIAL, batch_size: ~typing.Optional[int] = None, total_trials: ~typing.Optional[int] = None, tolerated_trial_failure_rate: float = 0.5, min_failed_trials_for_failure_rate_check: int = 5, log_filepath: ~typing.Optional[str] = None, logging_level: int = 20, ttl_seconds_for_trials: ~typing.Optional[int] = None, init_seconds_between_polls: ~typing.Optional[int] = 1, min_seconds_before_poll: float = 1.0, seconds_between_polls_backoff_factor: float = 1.5, timeout_hours: ~typing.Optional[float] = None, run_trials_in_batches: bool = False, debug_log_run_metadata: bool = False, early_stopping_strategy: ~typing.Optional[~ax.early_stopping.strategies.base.BaseEarlyStoppingStrategy] = None, global_stopping_strategy: ~typing.Optional[~ax.global_stopping.strategies.base.BaseGlobalStoppingStrategy] = None, suppress_storage_errors_after_retries: bool = False, wait_for_running_trials: bool = True, fetch_kwargs: ~typing.Dict[str, ~typing.Any] = <factory>, validate_metrics: bool = True, status_quo_weight: float = 0.0, enforce_immutable_search_space_and_opt_config: bool = True)[source]¶

Bases: object

Settings for a scheduler instance.

max_pending_trials¶

Maximum number of pending trials the scheduler can have STAGED or RUNNING at once, required. If looking to use Runner.poll_available_capacity as a primary guide for how many trials should be pending at a given time, set this limit to a high number, as an upper bound on number of trials that should not be exceeded.

Type:

int

trial_type¶

Type of trials (1-arm Trial or multi-arm Batch Trial) that will be deployed using the scheduler. Defaults to 1-arm Trial. NOTE: use BatchTrial only if need to evaluate multiple arms together, e.g. in an A/B-test influenced by data nonstationarity. For cases where just deploying multiple arms at once is beneficial but the trials are evaluated independently, implement run_trials method in scheduler subclass, to deploy multiple 1-arm trials at the same time.

Type:

ax.service.utils.scheduler_options.TrialType

batch_size¶

If using BatchTrial the number of arms to be generated and deployed per trial.

Type:

Optional[int]

total_trials¶

Limit on number of trials a given Scheduler should run. If no stopping criteria are implemented on a given scheduler, exhaustion of this number of trials will be used as default stopping criterion in Scheduler.run_all_trials. Required to be non-null if using Scheduler.run_all_trials (not required for Scheduler.run_n_trials).

Type:

Optional[int]

tolerated_trial_failure_rate¶

Fraction of trials in this optimization that are allowed to fail without the whole optimization ending. Expects value between 0 and 1. NOTE: Failure rate checks begin once min_failed_trials_for_failure_rate_check trials have failed; after that point if the ratio of failed trials to total trials ran so far exceeds the failure rate, the optimization will halt.

Type:

float

min_failed_trials_for_failure_rate_check¶

The minimum number of trials that must fail in Scheduler in order to start checking failure rate.

Type:

int

log_filepath¶

File, to which to write optimization logs.

Type:

Optional[str]

logging_level¶

Minimum level of logging statements to log, defaults to logging.INFO.

Type:

int

ttl_seconds_for_trials¶

Optional TTL for all trials created within this Scheduler, in seconds. Trials that remain RUNNING for more than their TTL seconds will be marked FAILED once the TTL elapses and may be re-suggested by the Ax optimization models.

Type:

Optional[int]

init_seconds_between_polls¶

Initial wait between rounds of polling, in seconds. Relevant if using the default wait- for-completed-runs functionality of the base Scheduler (if wait_for_completed_trials_and_report_results is not overridden). With the default waiting, every time a poll returns that no trial evaluations completed, wait time will increase; once some completed trial evaluations are found, it will reset back to this value. Specify 0 to not introduce any wait between polls.

Type:

Optional[int]

min_seconds_before_poll¶

Minimum number of seconds between beginning to run a trial and the first poll to check trial status.

Type:

float

timeout_hours¶

Number of hours after which the optimization will abort.

Type:

Optional[float]

seconds_between_polls_backoff_factor¶

The rate at which the poll interval increases.

Type:

float

run_trials_in_batches¶

If True and poll_available_capacity is implemented to return non-null results, trials will be dispatched in groups via run_trials instead of one-by-one via run_trial. This allows to save time, IO calls or computation in cases where dispatching trials in groups is more efficient then sequential deployment. The size of the groups will be determined as the minimum of self.poll_available_capacity() and the number of generator runs that the generation strategy is able to produce without more data or reaching its allowed max paralellism limit.

Type:

bool

debug_log_run_metadata¶

Whether to log run_metadata for debugging purposes.

Type:

bool

early_stopping_strategy¶

A BaseEarlyStoppingStrategy that determines whether a trial should be stopped given the current state of the experiment. Used in should_stop_trials_early.

Type:

Optional[ax.early_stopping.strategies.base.BaseEarlyStoppingStrategy]

global_stopping_strategy¶

A BaseGlobalStoppingStrategy that determines whether the full optimization should be stopped or not.

Type:

Optional[ax.global_stopping.strategies.base.BaseGlobalStoppingStrategy]

suppress_storage_errors_after_retries¶

Whether to fully suppress SQL storage-related errors if encountered, after retrying the call multiple times. Only use if SQL storage is not important for the given use case, since this will only log, but not raise, an exception if it’s encountered while saving to DB or loading from it.

Type:

bool

wait_for_running_trials¶

Whether the scheduler should wait for running trials or exit.

Type:

bool

fetch_kwargs¶

Kwargs to be used when fetching data.

Type:

Dict[str, Any]

validate_metrics¶

Whether to raise an error if there is a problem with the metrics attached to the experiment.

Type:

bool

status_quo_weight¶

The weight of the status quo arm. This is only used if the scheduler is using a BatchTrial. This requires that the status_quo be set on the experiment.

Type:

float

enforce_immutable_search_space_and_opt_config¶

Whether to enforce that the search space and optimization config are immutable. If true, will add “immutable_search_space_and_opt_config”: True to experiment properties

Type:

bool

batch_size: Optional[int] = None¶

debug_log_run_metadata: bool = False¶

early_stopping_strategy: Optional[BaseEarlyStoppingStrategy] = None¶

enforce_immutable_search_space_and_opt_config: bool = True¶

fetch_kwargs: Dict[str, Any]¶

global_stopping_strategy: Optional[BaseGlobalStoppingStrategy] = None¶

init_seconds_between_polls: Optional[int] = 1¶

log_filepath: Optional[str] = None¶

logging_level: int = 20¶

max_pending_trials: int = 10¶

min_failed_trials_for_failure_rate_check: int = 5¶

min_seconds_before_poll: float = 1.0¶

run_trials_in_batches: bool = False¶

seconds_between_polls_backoff_factor: float = 1.5¶

status_quo_weight: float = 0.0¶

suppress_storage_errors_after_retries: bool = False¶

timeout_hours: Optional[float] = None¶

tolerated_trial_failure_rate: float = 0.5¶

total_trials: Optional[int] = None¶

trial_type: TrialType = 0¶

ttl_seconds_for_trials: Optional[int] = None¶

validate_metrics: bool = True¶

wait_for_running_trials: bool = True¶

class ax.service.utils.scheduler_options.TrialType(value)[source]¶

Bases: Enum

An enumeration.

BATCH_TRIAL = 1¶

TRIAL = 0¶

Best Point Identification¶

class ax.service.utils.best_point_mixin.BestPointMixin[source]¶

Bases: object

get_best_parameters(optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None, use_model_predictions: bool = True) → Optional[Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Identifies the best parameterization tried in the experiment so far.

First attempts to do so with the model used in optimization and its corresponding predictions if available. Falls back to the best raw objective based on the data fetched from the experiment.

NOTE: TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

• use_model_predictions – Whether to extract the best point using model predictions or directly observed values. If True, the metric means and covariances in this method’s output will also be based on model predictions and may differ from the observed values.

Returns:

Tuple of parameterization and model predictions for it.

abstract get_best_trial(optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None, use_model_predictions: bool = True) → Optional[Tuple[int, Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Identifies the best parameterization tried in the experiment so far.

First attempts to do so with the model used in optimization and its corresponding predictions if available. Falls back to the best raw objective based on the data fetched from the experiment.

NOTE: TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

• use_model_predictions – Whether to extract the best point using model predictions or directly observed values. If True, the metric means and covariances in this method’s output will also be based on model predictions and may differ from the observed values.

Returns:

Tuple of trial index, parameterization and model predictions for it.

abstract get_hypervolume(optimization_config: Optional[MultiObjectiveOptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None, use_model_predictions: bool = True) → float[source]¶

Calculate hypervolume of a pareto frontier based on either the posterior means of given observation features or observed data.

Parameters:

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

• use_model_predictions – Whether to extract the Pareto frontier using model predictions or directly observed values. If True, the metric means and covariances in this method’s output will also be based on model predictions and may differ from the observed values.

abstract get_pareto_optimal_parameters(optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None, use_model_predictions: bool = True) → Optional[Dict[int, Tuple[Dict[str, Union[None, str, bool, float, int]], Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Identifies the best parameterizations tried in the experiment so far, using model predictions if use_model_predictions is true and using observed values from the experiment otherwise. By default, uses model predictions to account for observation noise.

NOTE: The format of this method’s output is as follows: { trial_index –> (parameterization, (means, covariances) }, where means are a dictionary of form { metric_name –> metric_mean } and covariances are a nested dictionary of form { one_metric_name –> { another_metric_name: covariance } }.

Parameters:

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

• use_model_predictions – Whether to extract the Pareto frontier using model predictions or directly observed values. If True, the metric means and covariances in this method’s output will also be based on model predictions and may differ from the observed values.

Returns:

None if it was not possible to extract the Pareto frontier, otherwise a mapping from trial index to the tuple of: - the parameterization of the arm in that trial, - two-item tuple of metric means dictionary and covariance matrix

(model-predicted if use_model_predictions=True and observed otherwise).

abstract get_trace() → List[float][source]¶

Get the optimization trace of the given experiment.

The output is equivalent to calling _get_hypervolume or _get_best_trial repeatedly, with an increasing sequence of trial_indices and with use_model_predictions = False, though this does it more efficiently.

Parameters:

• experiment – The experiment to get the trace for.

• optimization_config – An optional optimization config to use for computing the trace. This allows computing the traces under different objectives or constraints without having to modify the experiment.

Returns:

A list of observed hypervolumes or best values.

abstract get_trace_by_progression(bins: Optional[List[float]] = None, final_progression_only: bool = False) → Tuple[List[float], List[float]][source]¶

Get the optimization trace with respect to trial progressions instead of trial_indices (which is the behavior used in get_trace). Note that this method does not take into account the parallelism of trials and essentially assumes that trials are run one after another, in the sense that it considers the total number of progressions “used” at the end of trial k to be the cumulative progressions “used” in trials 0,…,k. This method assumes that the final value of a particular trial is used and does not take the best value of a trial over its progressions.

The best observed value is computed at each value in bins (see below for details). If bins is not supplied, the method defaults to a heuristic of approximately NUM_BINS_PER_TRIAL per trial, where each trial is assumed to run until maximum progression (inferred from the data).

Parameters:

• experiment – The experiment to get the trace for.

• optimization_config – An optional optimization config to use for computing the trace. This allows computing the traces under different objectives or constraints without having to modify the experiment.

• bins – A list progression values at which to calculate the best observed value. The best observed value at bins[i] is defined as the value observed in trials 0,…,j where j = largest trial such that the total progression in trials 0,…,j is less than bins[i].

• final_progression_only – If True, considers the value of the last step to be the value of the trial. If False, considers the best along the curve to be the value of the trial.

Returns:

A tuple containing (1) the list of observed hypervolumes or best values and (2) a list of associated x-values (i.e., progressions) useful for plotting.

ax.service.utils.best_point.extract_Y_from_data(experiment: Experiment, metric_names: List[str], data: Optional[Data] = None) → Tuple[Tensor, Tensor][source]¶

Converts the experiment observation data into a tensor.

NOTE: This requires block design for observations. It will error out if any trial is missing data for any of the given metrics or if the data is missing the trial_index.

Parameters:

• experiment – The experiment to extract the data from.

• metric_names – List of metric names to extract data for.

• data – An optional Data object to use instead of the experiment data. Note that the experiment must have a corresponding COMPLETED or EARLY_STOPPED trial for each trial_index in the data.

Returns:

A two-element Tuple containing a tensor of observed metrics and a tensor of trial_indices.

ax.service.utils.best_point.fill_missing_thresholds_from_nadir(experiment: Experiment, optimization_config: OptimizationConfig) → List[ObjectiveThreshold][source]¶

Get the objective thresholds from the optimization config and fill the missing thresholds based on the nadir point.

Parameters:

• experiment – The experiment, whose data is used to calculate the nadir point.

• optimization_config – Optimization config to get the objective thresholds and the objective directions from.

Returns:

A list of objective thresholds, one for each objective in optimization config.

ax.service.utils.best_point.get_best_by_raw_objective(experiment: Experiment, optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, identifies the arm that had the best raw objective, based on the data fetched from the experiment.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of parameterization, and model predictions for it.

ax.service.utils.best_point.get_best_by_raw_objective_with_trial_index(experiment: Experiment, optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[int, Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, identifies the arm that had the best raw objective, based on the data fetched from the experiment.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of trial index, parameterization, and model predictions for it.

ax.service.utils.best_point.get_best_parameters(experiment: Experiment, models_enum: Type[ModelRegistryBase], optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, identifies the best arm.

First attempts according to do so with models used in optimization and its corresponding predictions if available. Falls back to the best raw objective based on the data fetched from the experiment.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• models_enum – Registry of all models that may be in the experiment’s generation strategy.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of parameterization and model predictions for it.

ax.service.utils.best_point.get_best_parameters_from_model_predictions(experiment: Experiment, models_enum: Type[ModelRegistryBase], trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, returns the best predicted parameterization and corresponding prediction based on the most recent Trial with predictions. If no trials have predictions returns None.

Only some models return predictions. For instance GPEI does while Sobol does not.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• models_enum – Registry of all models that may be in the experiment’s generation strategy.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of parameterization and model predictions for it.

ax.service.utils.best_point.get_best_parameters_from_model_predictions_with_trial_index(experiment: Experiment, models_enum: Type[ModelRegistryBase], optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[int, Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, returns the best predicted parameterization and corresponding prediction based on the most recent Trial with predictions. If no trials have predictions returns None.

Only some models return predictions. For instance GPEI does while Sobol does not.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• models_enum – Registry of all models that may be in the experiment’s generation strategy.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of trial index, parameterization, and model predictions for it.

ax.service.utils.best_point.get_best_parameters_with_trial_index(experiment: Experiment, models_enum: Type[ModelRegistryBase], optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Optional[Tuple[int, Dict[str, Union[None, str, bool, float, int]], Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]]]][source]¶

Given an experiment, identifies the best arm.

First attempts according to do so with models used in optimization and its corresponding predictions if available. Falls back to the best raw objective based on the data fetched from the experiment.

TModelPredictArm is of the form:

({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})

Parameters:

• experiment – Experiment, on which to identify best raw objective arm.

• models_enum – Registry of all models that may be in the experiment’s generation strategy.

• optimization_config – Optimization config to use in place of the one stored on the experiment.

• trial_indices – Indices of trials for which to retrieve data. If None will retrieve data from all available trials.

Returns:

Tuple of trial index, parameterization, and model predictions for it.

ax.service.utils.best_point.get_best_raw_objective_point(experiment: Experiment, optimization_config: Optional[OptimizationConfig] = None, trial_indices: Optional[Iterable[int]] = None) → Tuple[Dict[str, Union[None, str, bool, float, int]], Dict[str, Tuple[float, float]]][source]¶