ax.modelbridge¶

Generation Strategy¶

class ax.modelbridge.generation_strategy.GenerationStep(model: Union[ModelRegistryBase, Callable[…, ModelBridge]], num_trials: int, min_trials_observed: int = 0, max_parallelism: Optional[int] = None, use_update: bool = False, enforce_num_trials: bool = True, model_kwargs: Optional[Dict[str, Any]] = None, model_gen_kwargs: Optional[Dict[str, Any]] = None, index: int = - 1)[source]¶

Bases: tuple

One step in the generation strategy, corresponds to a single model. Describes the model, how many trials will be generated with this model, what minimum number of observations is required to proceed to the next model, etc.

NOTE: Model can be specified either from the model registry (ax.modelbridge.registry.Models or using a callable model constructor. Only models from the registry can be saved, and thus optimization can only be resumed if interrupted when using models from the registry.

Parameters

• model – A member of Models enum or a callable returning an instance of ModelBridge with an instantiated underlying Model. Refer to ax/modelbridge/factory.py for examples of such callables.

• num_trials – How many trials to generate with the model from this step. If set to -1, trials will continue to be generated from this model as long as generation_strategy.gen is called (available only for the last of the generation steps).

• min_trials_observed – How many trials must be completed before the generation strategy can proceed to the next step. Defaults to 0. If num_trials of a given step have been generated but min_trials_ observed have not been completed, a call to generation_strategy.gen will fail with a DataRequiredError.

• max_parallelism – How many trials generated in the course of this step are allowed to be run (i.e. have trial.status of RUNNING) simultaneously. If max_parallelism trials from this step are already running, a call to generation_strategy.gen will fail with a MaxParallelismReached Exception, indicating that more trials need to be completed before generating and running next trials.

• use_update – Whether to use model_bridge.update instead or reinstantiating model + bridge on every call to gen within a single generation step. NOTE: use of update on stateful models that do not implement _get_state may result in inability to correctly resume a generation strategy from a serialized state.

• enforce_num_trials – Whether to enforce that only num_trials are generated from the given step. If False and num_trials have been generated, but min_trials_observed have not been completed, generation_strategy.gen will continue generating trials from the current step, exceeding num_ trials for it. Allows to avoid DataRequiredError, but delays proceeding to next generation step.

• model_kwargs – Dictionary of kwargs to pass into the model constructor on instantiation. E.g. if model is Models.SOBOL, kwargs will be applied as Models.SOBOL(**model_kwargs); if model is get_sobol, get_sobol( **model_kwargs). NOTE: if generation strategy is interrupted and resumed from a stored snapshot and its last used model has state saved on its generator runs, model_kwargs is updated with the state dict of the model, retrieved from the last generator run of this generation strategy.

• model_gen_kwargs – Each call to generation_strategy.gen performs a call to the step’s model’s gen under the hood; model_gen_kwargs will be passed to the model’s gen like so: model.gen(**model_gen_kwargs).

• index – Index of this generation step, for use internally in Generation Strategy. Do not assign as it will be reassigned when instantiating GenerationStrategy with a list of its steps.

property enforce_num_trials¶

Alias for field number 5

property index¶

Alias for field number 8

property max_parallelism¶

Alias for field number 3

property min_trials_observed¶

Alias for field number 2

property model¶

Alias for field number 0

property model_gen_kwargs¶

Alias for field number 7

property model_kwargs¶

Alias for field number 6

property model_name¶

property num_trials¶

Alias for field number 1

property use_update¶

Alias for field number 4

class ax.modelbridge.generation_strategy.GenerationStrategy(steps: List[ax.modelbridge.generation_strategy.GenerationStep], name: Optional[str] = None)[source]¶

Bases: ax.utils.common.base.Base

GenerationStrategy describes which model should be used to generate new points for which trials, enabling and automating use of different models throughout the optimization process. For instance, it allows to use one model for the initialization trials, and another one for all subsequent trials. In the general case, this allows to automate use of an arbitrary number of models to generate an arbitrary numbers of trials described in the trials_per_model argument.

Parameters

• steps – A list of GenerationStep describing steps of this strategy.

• name – An optional name for this generaiton strategy. If not specified, strategy’s name will be names of its steps’ models joined with ‘+’.

clone_reset() → GenerationStrategy[source]¶

Copy this generation strategy without it’s state.

property experiment¶

Experiment, currently set on this generation strategy.

gen(experiment: ax.core.experiment.Experiment, data: Optional[ax.core.data.Data] = None, n: int = 1, pending_observations: Optional[Dict[str, List[ax.core.observation.ObservationFeatures]]] = None, **kwargs: Any) → ax.core.generator_run.GeneratorRun[source]¶

Produce the next points in the experiment. Additional kwargs passed to this method are propagated directly to the underlying model’s gen, along with the model_gen_kwargs set on the current generation step.

NOTE: Each generator run returned from this function must become a single trial on the experiment to comply with assumptions made in generation strategy. Do not split one generator run produced from generation strategy into multiple trials (never making a generator run into a trial is allowed).

Parameters

• experiment – Experiment, for which the generation strategy is producing a new generator run in the course of gen, and to which that generator run will be added as trial(s). Information stored on the experiment (e.g., trial statuses) is used to determine which model will be used to produce the generator run returned from this method.

• data – Optional data to be passed to the underlying model’s gen, which is called within this method and actually produces the resulting generator run. By default, data is all data on the experiment if use_update is False and only the new data since the last call to this method if use_update is True.

• n – Integer representing how many arms should be in the generator run produced by this method. NOTE: Some underlying models may ignore the n and produce a model-determined number of arms. In that case this method will also output a generator run with number of arms that can differ from n.

• pending_observations – A map from metric name to pending observations for that metric, used by some models to avoid resuggesting points that are currently being evaluated.

property last_generator_run¶

Latest generator run produced by this generation strategy. Returns None if no generator runs have been produced yet.

property model¶

Current model in this strategy. Returns None if no model has been set yet (i.e., if no generator runs have been produced from this GS).

property model_transitions¶

List of trial indices where a transition happened from one model to another.

property name¶

Name of this generation strategy. Defaults to a combination of model names provided in generation steps.

property num_can_complete_this_step¶

Number of trials for the current step in generation strategy that can be completed (so are not in status FAILED or ABANDONED). Used to keep track of how many generator runs (that become trials) can be produced from the current generation step.

NOTE: This includes COMPLETED trials.

property num_completed_this_step¶

Number of trials in status COMPLETD for the current generation step of this strategy.

property num_running_trials_this_step¶

Number of trials in status RUNNING for the current generation step of this strategy.

property trial_indices_by_step¶

Find trials in experiment that are not mapped to a generation step yet and add them to the mapping of trials by generation step.

property trials_as_df¶

Puts information on individual trials into a data frame for easy viewing. For example: Gen. Step | Model | Trial Index | Trial Status | Arm Parameterizations 0 | Sobol | 0 | RUNNING | {“0_0”:{“x”:9.17…}}

property uses_non_registered_models¶

Whether this generation strategy involves models that are not registered and therefore cannot be stored.

Registry¶

class ax.modelbridge.registry.ModelRegistryBase(value)[source]¶

Bases: enum.Enum

Base enum that provides instrumentation of __call__ on enum values, for enums that link their values to ModelSetup-s like Models.

property model_bridge_class¶

Type of ModelBridge used for the given model+bridge setup.

property model_class¶

Type of Model used for the given model+bridge setup.

view_defaults() → Tuple[Dict[str, Any], Dict[str, Any]][source]¶

Obtains the default keyword arguments for the model and the modelbridge specified through the Models enum, for ease of use in notebook environment, since models and bridges cannot be inspected directly through the enum.

Returns

A tuple of default keyword arguments for the model and the model bridge.

view_kwargs() → Tuple[Dict[str, Any], Dict[str, Any]][source]¶

Obtains annotated keyword arguments that the model and the modelbridge (corresponding to a given member of the Models enum) constructors expect.

Returns

A tuple of annotated keyword arguments for the model and the model bridge.

class ax.modelbridge.registry.ModelSetup(bridge_class: Type[ModelBridge], model_class: Type[Model], transforms: List[Type[Transform]], standard_bridge_kwargs: Optional[Dict[str, Any]] = None, not_saved_model_kwargs: Optional[List[str]] = None)[source]¶

Bases: tuple

A model setup defines a coupled combination of a model, a model bridge, standard set of transforms, and standard model bridge keyword arguments. This coupled combination yields a given standard modeling strategy in Ax, such as BoTorch GP+EI, a Thompson sampler, or a Sobol quasirandom generator.

property bridge_class¶

Alias for field number 0

property model_class¶

Alias for field number 1

property not_saved_model_kwargs¶

Alias for field number 4

property standard_bridge_kwargs¶

Alias for field number 3

property transforms¶

Alias for field number 2

class ax.modelbridge.registry.Models(value)[source]¶

Bases: ax.modelbridge.registry.ModelRegistryBase

Registry of available models.

Uses MODEL_KEY_TO_MODEL_SETUP to retrieve settings for model and model bridge, by the key stored in the enum value.

To instantiate a model in this enum, simply call an enum member like so: Models.SOBOL(search_space=search_space) or Models.GPEI(experiment=experiment, data=data). Keyword arguments specified to the call will be passed into the model or the model bridge constructors according to their keyword.

For instance, Models.SOBOL(search_space=search_space, scramble=False) will instantiate a RandomModelBridge(search_space=search_space) with a SobolGenerator(scramble=False) underlying model.

BOTORCH = 'BO'¶

BOTORCH_MODULAR = 'BoTorch'¶

EMPIRICAL_BAYES_THOMPSON = 'EB'¶

FACTORIAL = 'Factorial'¶

GPEI = 'GPEI'¶

GPKG = 'GPKG'¶

GPMES = 'GPMES'¶

MOO = 'MOO'¶

SOBOL = 'Sobol'¶

THOMPSON = 'Thompson'¶

UNIFORM = 'Uniform'¶

ax.modelbridge.registry.get_model_from_generator_run(generator_run: ax.core.generator_run.GeneratorRun, experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, models_enum: Optional[Type[ax.modelbridge.registry.ModelRegistryBase]] = None, after_gen: bool = True) → ax.modelbridge.base.ModelBridge[source]¶

Reinstantiate a model from model key and kwargs stored on a given generator run, with the given experiment and the data to initialize the model with.

Note: requires that the model that was used to get the generator run, is part of the Models registry enum.

Parameters

• generator_run – A GeneratorRun created by the model we are looking to reinstantiate.

• experiment – The experiment for which the model is reinstantiated.

• data – Data, with which to reinstantiate the model.

• models_enum – Optional subclass of Models registry, from which to obtain the settings of the model. Useful only if the generator run was created via a model that could not be included into the main registry, but can still be represented as a ModelSetup and was added to a registry that extends Models.

• after_gen – Whether to reinstantiate the model in the state, in which it was after it created this generator run, as opposed to before. Defaults to True, useful when reinstantiating the model to resume optimization, rather than to recreate its state at the time of generation. TO recreate state at the time of generation, set to False.

ax.modelbridge.registry.logger = <Logger ax.modelbridge.registry (DEBUG)>¶

Module containing a registry of standard models (and generators, samplers etc.) such as Sobol generator, GP+EI, Thompson sampler, etc.

Use of Models enum allows for serialization and reinstantiation of models and generation strategies from generator runs they produced. To reinstantiate a model from generator run, use get_model_from_generator_run utility from this module.

Factory¶

ax.modelbridge.factory.DEFAULT_EHVI_BATCH_LIMIT = 5¶

Module containing functions that generate standard models, such as Sobol, GP+EI, etc.

Note: a special case here is a composite generator, which requires an additional GenerationStrategy and is able to delegate work to multiple models (for instance, to a random model to generate the first trial, and to an optimization model for subsequent trials).

ax.modelbridge.factory.get_GPEI(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu')) → ax.modelbridge.torch.TorchModelBridge[source]¶

Instantiates a GP model that generates points with EI.

ax.modelbridge.factory.get_GPKG(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, cost_intercept: float = 0.01, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu'), transforms: List[Type[ax.modelbridge.transforms.base.Transform]] = [<class 'ax.modelbridge.transforms.remove_fixed.RemoveFixed'>, <class 'ax.modelbridge.transforms.ordered_choice_encode.OrderedChoiceEncode'>, <class 'ax.modelbridge.transforms.one_hot.OneHot'>, <class 'ax.modelbridge.transforms.int_to_float.IntToFloat'>, <class 'ax.modelbridge.transforms.log.Log'>, <class 'ax.modelbridge.transforms.unit_x.UnitX'>, <class 'ax.modelbridge.transforms.ivw.IVW'>, <class 'ax.modelbridge.transforms.derelativize.Derelativize'>, <class 'ax.modelbridge.transforms.standardize_y.StandardizeY'>], transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, **kwargs: Any) → ax.modelbridge.torch.TorchModelBridge[source]¶

Instantiates a GP model that generates points with KG.

ax.modelbridge.factory.get_GPMES(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, cost_intercept: float = 0.01, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu'), transforms: List[Type[ax.modelbridge.transforms.base.Transform]] = [<class 'ax.modelbridge.transforms.remove_fixed.RemoveFixed'>, <class 'ax.modelbridge.transforms.ordered_choice_encode.OrderedChoiceEncode'>, <class 'ax.modelbridge.transforms.one_hot.OneHot'>, <class 'ax.modelbridge.transforms.int_to_float.IntToFloat'>, <class 'ax.modelbridge.transforms.log.Log'>, <class 'ax.modelbridge.transforms.unit_x.UnitX'>, <class 'ax.modelbridge.transforms.ivw.IVW'>, <class 'ax.modelbridge.transforms.derelativize.Derelativize'>, <class 'ax.modelbridge.transforms.standardize_y.StandardizeY'>], transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, **kwargs: Any) → ax.modelbridge.torch.TorchModelBridge[source]¶

Instantiates a GP model that generates points with MES.

ax.modelbridge.factory.get_MOO_EHVI(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, objective_thresholds: Optional[List[ax.core.outcome_constraint.ObjectiveThreshold]] = None, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: Optional[torch.device] = None) → ax.modelbridge.multi_objective_torch.MultiObjectiveTorchModelBridge[source]¶

Instantiates a multi-objective model that generates points with EHVI.

Requires objective_thresholds, a list of ax.core.ObjectiveThresholds, for every objective being optimized. An arm only improves hypervolume if it is strictly better than all objective thresholds.

objective_thresholds can be passed in the optimization_config or passed directly here.

ax.modelbridge.factory.get_MOO_PAREGO(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, objective_thresholds: Optional[List[ax.core.outcome_constraint.ObjectiveThreshold]] = None, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu')) → ax.modelbridge.multi_objective_torch.MultiObjectiveTorchModelBridge[source]¶

Instantiates a multi-objective model that generates points with ParEGO.

qParEGO optimizes random augmented chebyshev scalarizations of the multiple objectives. This allows it to explore non-convex pareto frontiers.

ax.modelbridge.factory.get_MOO_RS(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, objective_thresholds: Optional[List[ax.core.outcome_constraint.ObjectiveThreshold]] = None, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu')) → ax.modelbridge.multi_objective_torch.MultiObjectiveTorchModelBridge[source]¶

Instantiates a Random Scalarization multi-objective model.

Chooses a different random linear scalarization of the objectives for generating each new candidate arm. This will only explore the convex hull of the pareto frontier.

ax.modelbridge.factory.get_MTGP(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, trial_index: Optional[int] = None) → ax.modelbridge.torch.TorchModelBridge[source]¶

Instantiates a Multi-task Gaussian Process (MTGP) model that generates points with EI.

If the input experiment is a MultiTypeExperiment then a Multi-type Multi-task GP model will be instantiated. Otherwise, the model will be a Single-type Multi-task GP.

ax.modelbridge.factory.get_MTGP_PAREGO(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, trial_index: Optional[int] = None, objective_thresholds: Optional[List[ax.core.outcome_constraint.ObjectiveThreshold]] = None, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu')) → ax.modelbridge.multi_objective_torch.MultiObjectiveTorchModelBridge[source]¶

Instantiates a multi-objective, multi-task model that uses qParEGO.

qParEGO optimizes random augmented chebyshev scalarizations of the multiple objectives. This allows it to explore non-convex pareto frontiers.

ax.modelbridge.factory.get_botorch(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, dtype: torch.dtype = torch.float64, device: torch.device = device(type='cpu'), transforms: List[Type[ax.modelbridge.transforms.base.Transform]] = [<class 'ax.modelbridge.transforms.remove_fixed.RemoveFixed'>, <class 'ax.modelbridge.transforms.ordered_choice_encode.OrderedChoiceEncode'>, <class 'ax.modelbridge.transforms.one_hot.OneHot'>, <class 'ax.modelbridge.transforms.int_to_float.IntToFloat'>, <class 'ax.modelbridge.transforms.log.Log'>, <class 'ax.modelbridge.transforms.unit_x.UnitX'>, <class 'ax.modelbridge.transforms.ivw.IVW'>, <class 'ax.modelbridge.transforms.derelativize.Derelativize'>, <class 'ax.modelbridge.transforms.standardize_y.StandardizeY'>], transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, model_constructor: Callable[[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor], List[int], List[int], List[str], Optional[Dict[str, torch.Tensor]], Any], botorch.models.model.Model] = <function get_and_fit_model>, model_predictor: Callable[[botorch.models.model.Model, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]] = <function predict_from_model>, acqf_constructor: Callable[[botorch.models.model.Model, torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]], Optional[torch.Tensor], Optional[torch.Tensor], Any], botorch.acquisition.acquisition.AcquisitionFunction] = <function get_NEI>, acqf_optimizer: Callable[[botorch.acquisition.acquisition.AcquisitionFunction, torch.Tensor, int, Optional[List[Tuple[torch.Tensor, torch.Tensor, float]]], Optional[Dict[int, float]], Optional[Callable[[torch.Tensor], torch.Tensor]], Any], Tuple[torch.Tensor, torch.Tensor]] = <function scipy_optimizer>, refit_on_cv: bool = False, refit_on_update: bool = True, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None) → ax.modelbridge.torch.TorchModelBridge[source]¶

Instantiates a BotorchModel.

ax.modelbridge.factory.get_empirical_bayes_thompson(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, num_samples: int = 10000, min_weight: Optional[float] = None, uniform_weights: bool = False) → ax.modelbridge.discrete.DiscreteModelBridge[source]¶

Instantiates an empirical Bayes / Thompson sampling model.

ax.modelbridge.factory.get_factorial(search_space: ax.core.search_space.SearchSpace) → ax.modelbridge.discrete.DiscreteModelBridge[source]¶

Instantiates a factorial generator.

ax.modelbridge.factory.get_sobol(search_space: ax.core.search_space.SearchSpace, seed: Optional[int] = None, deduplicate: bool = False, init_position: int = 0, scramble: bool = True) → ax.modelbridge.random.RandomModelBridge[source]¶

Instantiates a Sobol sequence quasi-random generator.

Parameters

• search_space – Sobol generator search space.

• kwargs – Custom args for sobol generator.

Returns

RandomModelBridge, with SobolGenerator as model.

ax.modelbridge.factory.get_thompson(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, search_space: Optional[ax.core.search_space.SearchSpace] = None, num_samples: int = 10000, min_weight: Optional[float] = None, uniform_weights: bool = False) → ax.modelbridge.discrete.DiscreteModelBridge[source]¶

Instantiates a Thompson sampling model.

ax.modelbridge.factory.get_uniform(search_space: ax.core.search_space.SearchSpace, deduplicate: bool = False, seed: Optional[int] = None) → ax.modelbridge.random.RandomModelBridge[source]¶

Instantiate uniform generator.

Parameters

• search_space – Uniform generator search space.

• kwargs – Custom args for uniform generator.

Returns

RandomModelBridge, with UniformGenerator as model.

Base Model Bridge¶

class ax.modelbridge.base.ModelBridge(search_space: ax.core.search_space.SearchSpace, model: Any, transforms: Optional[List[Type[ax.modelbridge.transforms.base.Transform]]] = None, experiment: Optional[ax.core.experiment.Experiment] = None, data: Optional[ax.core.data.Data] = None, transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False)[source]¶

Bases: abc.ABC

The main object for using models in Ax.

ModelBridge specifies 3 methods for using models:

• predict: Make model predictions. This method is not optimized for speed and so should be used primarily for plotting or similar tasks and not inside an optimization loop.

• gen: Use the model to generate new candidates.

• cross_validate: Do cross validation to assess model predictions.

ModelBridge converts Ax types like Data and Arm to types that are meant to be consumed by the models. The data sent to the model will depend on the implementation of the subclass, which will specify the actual API for external model.

This class also applies a sequence of transforms to the input data and problem specification which can be used to ensure that the external model receives appropriate inputs.

Subclasses will implement what is here referred to as the “terminal transform,” which is a transform that changes types of the data and problem specification.

cross_validate(cv_training_data: List[ax.core.observation.Observation], cv_test_points: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationData][source]¶

Make a set of cross-validation predictions.

Parameters

• cv_training_data – The training data to use for cross validation.

• cv_test_points – The test points at which predictions will be made.

Returns

A list of predictions at the test points.

evaluate_acquisition_function(observation_features: List[ax.core.observation.ObservationFeatures]) → List[float][source]¶

Evaluate the acquisition function for given set of observation features.

Parameters

observation_features – A list of observation features, representing parameterizations, for which to evaluate the acquisition function.

Returns

A list of acquisition function values, in the same order as the input observation features.

feature_importances(metric_name: str) → Dict[str, float][source]¶

gen(n: int, search_space: Optional[ax.core.search_space.SearchSpace] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, pending_observations: Optional[Dict[str, List[ax.core.observation.ObservationFeatures]]] = None, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, model_gen_options: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None) → ax.core.generator_run.GeneratorRun[source]¶

Parameters

• n – Number of points to generate

• search_space – Search space

• optimization_config – Optimization config

• pending_observations – A map from metric name to pending observations for that metric.

• fixed_features – An ObservationFeatures object containing any features that should be fixed at specified values during generation.

• model_gen_options – A config dictionary that is passed along to the model.

get_training_data() → List[ax.core.observation.Observation][source]¶

A copy of the (untransformed) data with which the model was fit.

property metric_names¶

Metric names present in training data.

property model_space¶

SearchSpace used to fit model.

predict(observation_features: List[ax.core.observation.ObservationFeatures]) → Tuple[Dict[str, List[float]], Dict[str, Dict[str, List[float]]]][source]¶

Make model predictions (mean and covariance) for the given observation features.

Predictions are made for all outcomes. If an out-of-design observation can successfully be transformed, the predicted value will be returned. Othwerise, we will attempt to find that observation in the training data and return the raw value.

Parameters

observation_features – observation features

Returns

2-element tuple containing

• Dictionary from metric name to list of mean estimates, in same order as observation_features.

• Nested dictionary with cov[‘metric1’][‘metric2’] a list of cov(metric1@x, metric2@x) for x in observation_features.

property status_quo¶

Observation corresponding to status quo, if any.

property training_in_design¶

For each observation in the training data, a bool indicating if it is in-design for the model.

transform_observation_data(observation_data: List[ax.core.observation.ObservationData]) → Any[source]¶

Applies transforms to given observation features and returns them in the model space.

Parameters

observation_features – ObservationFeatures to be transformed.

Returns

Transformed values. This could be e.g. a torch Tensor, depending on the ModelBridge subclass.

transform_observation_features(observation_features: List[ax.core.observation.ObservationFeatures]) → Any[source]¶

Applies transforms to given observation features and returns them in the model space.

Parameters

observation_features – ObservationFeatures to be transformed.

Returns

Transformed values. This could be e.g. a torch Tensor, depending on the ModelBridge subclass.

transform_optimization_config(optimization_config: ax.core.optimization_config.OptimizationConfig, fixed_features: ax.core.observation.ObservationFeatures) → Any[source]¶

Applies transforms to given optimization config.

Parameters

• optimization_config – OptimizationConfig to transform.

• fixed_features – features which should not be transformed.

Returns

Transformed values. This could be e.g. a torch Tensor, depending on the ModelBridge subclass.

update(new_data: ax.core.data.Data, experiment: ax.core.experiment.Experiment) → None[source]¶

Update the model bridge and the underlying model with new data. This method should be used instead of fit, in cases where the underlying model does not need to be re-fit from scratch, but rather updated.

Note: update expects only new data (obtained since the model initialization or last update) to be passed in, not all data in the experiment.

Parameters

• new_data – Data from the experiment obtained since the last call to update.

• experiment – Experiment, in which this data was obtained.

ax.modelbridge.base.gen_arms(observation_features: List[ax.core.observation.ObservationFeatures], arms_by_signature: Optional[Dict[str, ax.core.arm.Arm]] = None) → Tuple[List[ax.core.arm.Arm], Optional[Dict[str, Optional[Dict[str, Any]]]]][source]¶

Converts observation features to a tuple of arms list and candidate metadata dict, where arm signatures are mapped to their respective candidate metadata.

ax.modelbridge.base.unwrap_observation_data(observation_data: List[ax.core.observation.ObservationData]) → Tuple[Dict[str, List[float]], Dict[str, Dict[str, List[float]]]][source]¶

Converts observation data to the format for model prediction outputs. That format assumes each observation data has the same set of metrics.

Array Model Bridge¶

class ax.modelbridge.array.ArrayModelBridge(search_space: ax.core.search_space.SearchSpace, model: Any, transforms: Optional[List[Type[ax.modelbridge.transforms.base.Transform]]] = None, experiment: Optional[ax.core.experiment.Experiment] = None, data: Optional[ax.core.data.Data] = None, transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False)[source]¶

Bases: ax.modelbridge.base.ModelBridge

A model bridge for using array-based models.

Requires that all non-task parameters have been transformed to RangeParameters with float type and no log scale. Task parameters must be transformed to RangeParameters with int type.

This will convert all parameter types to float and put data into arrays.

feature_importances(metric_name: str) → Dict[str, float][source]¶

model: Any¶

outcomes: List[str]¶

parameters: List[str]¶

ax.modelbridge.array.validate_optimization_config(optimization_config: ax.core.optimization_config.OptimizationConfig, outcomes: List[str]) → None[source]¶

Validate optimization config against model fitted outcomes.

Parameters

• optimization_config – Config to validate.

• outcomes – List of metric names w/ valid model fits.

Raises

ValueError if –

1. Relative constraints are found 2. Optimization metrics are not present in model fitted outcomes.

Discrete Model Bridge¶

class ax.modelbridge.discrete.DiscreteModelBridge(search_space: ax.core.search_space.SearchSpace, model: Any, transforms: Optional[List[Type[ax.modelbridge.transforms.base.Transform]]] = None, experiment: Optional[ax.core.experiment.Experiment] = None, data: Optional[ax.core.data.Data] = None, transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False)[source]¶

Bases: ax.modelbridge.base.ModelBridge

A model bridge for using models based on discrete parameters.

Requires that all parameters have been transformed to ChoiceParameters.

model: ax.models.discrete_base.DiscreteModel¶

outcomes: List[str]¶

parameters: List[str]¶

search_space: Optional[ax.core.search_space.SearchSpace]¶

NumPy Model Bridge¶

class ax.modelbridge.numpy.NumpyModelBridge(search_space: ax.core.search_space.SearchSpace, model: Any, transforms: Optional[List[Type[ax.modelbridge.transforms.base.Transform]]] = None, experiment: Optional[ax.core.experiment.Experiment] = None, data: Optional[ax.core.data.Data] = None, transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False)[source]¶

Bases: ax.modelbridge.array.ArrayModelBridge

A model bridge for using numpy array-based models.

This model bridge interfaces with NumpyModel.

Requires that all parameters have been transformed to RangeParameters or FixedParameters with float type and no log scale.

model: ax.models.numpy_base.NumpyModel¶

outcomes: List[str]¶

parameters: List[str]¶

Random Model Bridge¶

class ax.modelbridge.random.RandomModelBridge(search_space: ax.core.search_space.SearchSpace, model: Any, transforms: Optional[List[Type[ax.modelbridge.transforms.base.Transform]]] = None, experiment: Optional[ax.core.experiment.Experiment] = None, data: Optional[ax.core.data.Data] = None, transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False)[source]

Bases: ax.modelbridge.base.ModelBridge

A model bridge for using purely random ‘models’. Data and optimization configs are not required.

This model bridge interfaces with RandomModel.

model

A RandomModel used to generate candidates (note: this an awkward use of the word ‘model’).

Type

ax.models.random.base.RandomModel

parameters

Params found in search space on modelbridge init.

Type

List[str]

model: ax.models.random.base.RandomModel

parameters: List[str]

update(new_data: ax.core.data.Data, experiment: ax.core.experiment.Experiment) → None[source]

Update the model bridge and the underlying model with new data. This method should be used instead of fit, in cases where the underlying model does not need to be re-fit from scratch, but rather updated.

Note: update expects only new data (obtained since the model initialization or last update) to be passed in, not all data in the experiment.

Parameters

• new_data – Data from the experiment obtained since the last call to update.

• experiment – Experiment, in which this data was obtained.

Torch Model Bridge¶

class ax.modelbridge.torch.TorchModelBridge(experiment: ax.core.experiment.Experiment, search_space: ax.core.search_space.SearchSpace, data: ax.core.data.Data, model: ax.models.torch_base.TorchModel, transforms: List[Type[ax.modelbridge.transforms.base.Transform]], transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, torch_dtype: Optional[torch.dtype] = None, torch_device: Optional[torch.device] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, fit_out_of_design: bool = False, default_model_gen_options: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None)[source]¶

Bases: ax.modelbridge.array.ArrayModelBridge

A model bridge for using torch-based models.

Specifies an interface that is implemented by TorchModel. In particular, model should have methods fit, predict, and gen. See TorchModel for the API for each of these methods.

Requires that all parameters have been transformed to RangeParameters or FixedParameters with float type and no log scale.

This class converts Ax parameter types to torch tensors before passing them to the model.

model: Optional[ax.models.torch_base.TorchModel]¶

Multi-Objective Torch Model Bridge¶

class ax.modelbridge.multi_objective_torch.MultiObjectiveTorchModelBridge(experiment: ax.core.experiment.Experiment, search_space: ax.core.search_space.SearchSpace, data: ax.core.data.Data, model: ax.models.torch_base.TorchModel, transforms: List[Type[ax.modelbridge.transforms.base.Transform]], transform_configs: Optional[Dict[str, Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]]] = None, torch_dtype: Optional[torch.dtype] = None, torch_device: Optional[torch.device] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ax.core.observation.ObservationFeatures] = None, optimization_config: Optional[ax.core.optimization_config.MultiObjectiveOptimizationConfig] = None, fit_out_of_design: bool = False, objective_thresholds: Optional[List[ax.core.outcome_constraint.ObjectiveThreshold]] = None, default_model_gen_options: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None)[source]¶

Bases: ax.modelbridge.torch.TorchModelBridge

A model bridge for using multi-objective torch-based models.

Specifies an interface that is implemented by MultiObjectiveTorchModel. In particular, model should have methods fit, predict, and gen. See MultiObjectiveTorchModel for the API for each of these methods.

Requires that all parameters have been transformed to RangeParameters or FixedParameters with float type and no log scale.

This class converts Ax parameter types to torch tensors before passing them to the model.

gen(n: int, search_space: Optional[ax.core.search_space.SearchSpace] = None, optimization_config: Optional[ax.core.optimization_config.OptimizationConfig] = None, pending_observations: Optional[Dict[str, List[ax.core.observation.ObservationFeatures]]] = None, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, model_gen_options: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None) → ax.core.generator_run.GeneratorRun[source]¶

Parameters

• n – Number of points to generate

• search_space – Search space

• optimization_config – Optimization config

• pending_observations – A map from metric name to pending observations for that metric.

• fixed_features – An ObservationFeatures object containing any features that should be fixed at specified values during generation.

• model_gen_options – A config dictionary that is passed along to the model.

General Utilities¶

ax.modelbridge.modelbridge_utils.array_to_observation_data(f: numpy.ndarray, cov: numpy.ndarray, outcomes: List[str]) → List[ax.core.observation.ObservationData][source]¶

Convert arrays of model predictions to a list of ObservationData.

Parameters

• f – An (n x m) array

• cov – An (n x m x m) array

• outcomes – A list of d outcome names

Returns: A list of n ObservationData

ax.modelbridge.modelbridge_utils.clamp_observation_features(observation_features: List[ax.core.observation.ObservationFeatures], search_space: ax.core.search_space.SearchSpace) → List[ax.core.observation.ObservationFeatures][source]¶

ax.modelbridge.modelbridge_utils.extract_objective_thresholds(objective_thresholds: List[ax.core.outcome_constraint.ObjectiveThreshold], outcomes: List[str]) → numpy.ndarray[source]¶

Extracts objective thresholds’ values, in the order of outcomes.

The extracted array will be no greater than the number of values in the objective_thresholds, typically the same as number of objectives being optimized.

Parameters

• objective_thresholds – Reference Point to extract values from.

• outcomes – n-length list of names of metrics.

Returns

len(objective_thresholds)-length list of reference point coordinates

ax.modelbridge.modelbridge_utils.extract_objective_weights(objective: ax.core.objective.Objective, outcomes: List[str]) → numpy.ndarray[source]¶

Extract a weights for objectives.

Weights are for a maximization problem.

Give an objective weight to each modeled outcome. Outcomes that are modeled but not part of the objective get weight 0.

In the single metric case, the objective is given either +/- 1, depending on the minimize flag.

In the multiple metric case, each objective is given the input weight, multiplied by the minimize flag.

Parameters

• objective – Objective to extract weights from.

• outcomes – n-length list of names of metrics.

Returns

n-length list of weights.

ax.modelbridge.modelbridge_utils.extract_outcome_constraints(outcome_constraints: List[ax.core.outcome_constraint.OutcomeConstraint], outcomes: List[str]) → Optional[Tuple[numpy.ndarray, numpy.ndarray]][source]¶

ax.modelbridge.modelbridge_utils.extract_parameter_constraints(parameter_constraints: List[ax.core.parameter_constraint.ParameterConstraint], param_names: List[str]) → Optional[Tuple[numpy.ndarray, numpy.ndarray]][source]¶

Extract parameter constraints.

ax.modelbridge.modelbridge_utils.get_bounds_and_task(search_space: ax.core.search_space.SearchSpace, param_names: List[str]) → Tuple[List[Tuple[float, float]], List[int], Dict[int, float]][source]¶

Extract box bounds from a search space in the usual Scipy format. Identify integer parameters as task features.

ax.modelbridge.modelbridge_utils.get_fixed_features(fixed_features: ax.core.observation.ObservationFeatures, param_names: List[str]) → Optional[Dict[int, float]][source]¶

Reformat a set of fixed_features.

ax.modelbridge.modelbridge_utils.get_pending_observation_features(experiment: ax.core.experiment.Experiment, include_failed_as_pending: bool = False) → Optional[Dict[str, List[ax.core.observation.ObservationFeatures]]][source]¶

Computes a list of pending observation features (corresponding to arms that have been generated and deployed in the course of the experiment, but have not been completed with data).

Parameters

• experiment – Experiment, pending features on which we seek to compute.

• include_failed_as_pending – Whether to include failed trials as pending (for example, to avoid the model suggesting them again).

Returns

An optional mapping from metric names to a list of observation features, pending for that metric (i.e. do not have evaluation data for that metric). If there are no pending features for any of the metrics, return is None.

ax.modelbridge.modelbridge_utils.get_pending_observation_features_based_on_trial_status(experiment: ax.core.experiment.Experiment) → Optional[Dict[str, List[ax.core.observation.ObservationFeatures]]][source]¶

A faster analogue of get_pending_observation_features that makes assumptions about trials in experiment in order to speed up extraction of pending points.

Assumptions:

• All arms in all trials in STAGED, RUNNING and ABANDONED statuses are to be considered pending for all outcomes.

• All arms in all trials in other statuses are to be considered not pending for all outcomes.

This entails:

• No actual data-fetching for trials to determine whether arms in them are pending for specific outcomes.

• Even if data is present for some outcomes in RUNNING trials, their arms will still be considered pending for those outcomes.

NOTE: This function should not be used to extract pending features in field experiments, where arms in running trials should not be considered pending if there is data for those arms.

Parameters

experiment – Experiment, pending features on which we seek to compute.

Returns

An optional mapping from metric names to a list of observation features, pending for that metric (i.e. do not have evaluation data for that metric). If there are no pending features for any of the metrics, return is None.

ax.modelbridge.modelbridge_utils.hypervolume(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, observation_features: Optional[List[ObservationFeatures]] = None, observation_data: Optional[List[ObservationData]] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → float[source]¶

Helper function that computes hypervolume of a given list of outcomes.

ax.modelbridge.modelbridge_utils.observation_data_to_array(observation_data: List[ax.core.observation.ObservationData]) → Tuple[numpy.ndarray, numpy.ndarray][source]¶

Convert a list of Observation data to arrays.

Parameters

observation_data – A list of n ObservationData

Returns

An array of n ObservationData, each containing

• f: An (n x m) array

• cov: An (n x m x m) array

ax.modelbridge.modelbridge_utils.observation_features_to_array(parameters: List[str], obsf: List[ax.core.observation.ObservationFeatures]) → numpy.ndarray[source]¶

Convert a list of Observation features to arrays.

ax.modelbridge.modelbridge_utils.observed_hypervolume(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → float[source]¶

Calculate hypervolume of a pareto frontier based on observed data.

Given observed data, return the hypervolume of the pareto frontier formed from those outcomes.

Parameters

• modelbridge – Modelbridge that holds previous training data.

• objective_thresholds – point defining the origin of hyperrectangles that can contribute to hypervolume.

• observation_features – observation features to predict. Model’s training data used by default if unspecified.

• optimization_config – Optimization config

Returns

(float) calculated hypervolume.

ax.modelbridge.modelbridge_utils.observed_pareto_frontier(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → List[ObservationData][source]¶

Generate a pareto frontier based on observed data.

Given observed data, return those outcomes in the pareto frontier.

Parameters

• modelbridge – Modelbridge that holds previous training data.

• objective_thresholds – metric values bounding the region of interest in the objective outcome space.

• optimization_config – Optimization config

Returns

Data representing points on the pareto frontier.

ax.modelbridge.modelbridge_utils.pareto_frontier(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, observation_features: Optional[List[ObservationFeatures]] = None, observation_data: Optional[List[ObservationData]] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → List[ObservationData][source]¶

Helper that applies transforms and calls frontier_evaluator.

ax.modelbridge.modelbridge_utils.parse_observation_features(X: numpy.ndarray, param_names: List[str], candidate_metadata: Optional[List[Optional[Dict[str, Any]]]] = None) → List[ax.core.observation.ObservationFeatures][source]¶

Re-format raw model-generated candidates into ObservationFeatures.

Parameters

• param_names – List of param names.

• X – Raw np.ndarray of candidate values.

• candidate_metadata – Model’s metadata for candidates it produced.

Returns

List of candidates, represented as ObservationFeatures.

ax.modelbridge.modelbridge_utils.pending_observations_as_array(pending_observations: Dict[str, List[ax.core.observation.ObservationFeatures]], outcome_names: List[str], param_names: List[str]) → Optional[List[numpy.ndarray]][source]¶

Re-format pending observations.

Parameters

• pending_observations – List of raw numpy pending observations.

• outcome_names – List of outcome names.

• param_names – List fitted param names.

Returns

Filtered pending observations data, by outcome and param names.

ax.modelbridge.modelbridge_utils.predicted_hypervolume(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, observation_features: Optional[List[ObservationFeatures]] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → float[source]¶

Calculate hypervolume of a pareto frontier based on the posterior means of given observation features.

Given a model and features to evaluate calculate the hypervolume of the pareto frontier formed from their predicted outcomes.

Parameters

• modelbridge – Modelbridge used to predict metrics outcomes.

• objective_thresholds – point defining the origin of hyperrectangles that can contribute to hypervolume.

• observation_features – observation features to predict. Model’s training data used by default if unspecified.

• optimization_config – Optimization config

Returns

calculated hypervolume.

ax.modelbridge.modelbridge_utils.predicted_pareto_frontier(modelbridge: modelbridge_module.array.ArrayModelBridge, objective_thresholds: Optional[TRefPoint] = None, observation_features: Optional[List[ObservationFeatures]] = None, optimization_config: Optional[MultiObjectiveOptimizationConfig] = None) → List[ObservationData][source]¶

Generate a pareto frontier based on the posterior means of given observation features.

Given a model and features to evaluate use the model to predict which points lie on the pareto frontier.

Parameters

• modelbridge – Modelbridge used to predict metrics outcomes.

• objective_thresholds – metric values bounding the region of interest in the objective outcome space.

• observation_features – observation features to predict. Model’s training data used by default if unspecified.

• optimization_config – Optimization config

Returns

Data representing points on the pareto frontier.

ax.modelbridge.modelbridge_utils.transform_callback(param_names: List[str], transforms: MutableMapping[str, ax.modelbridge.transforms.base.Transform]) → Callable[[numpy.ndarray], numpy.ndarray][source]¶

A closure for performing the round trip transformations.

The function round points by de-transforming points back into the original space (done by applying transforms in reverse), and then re-transforming them. This function is specifically for points which are formatted as numpy arrays. This function is passed to _model_gen.

Parameters

• param_names – Names of parameters to transform.

• transforms – Ordered set of transforms which were applied to the points.

Returns

a function with for performing the roundtrip transform.

ax.modelbridge.modelbridge_utils.validate_and_apply_final_transform(objective_weights: numpy.ndarray, outcome_constraints: Optional[Tuple[numpy.ndarray, numpy.ndarray]], linear_constraints: Optional[Tuple[numpy.ndarray, numpy.ndarray]], pending_observations: Optional[List[numpy.ndarray]], final_transform: Callable[[numpy.ndarray], torch.Tensor] = <built-in method tensor of type object>) → Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]], Optional[Tuple[torch.Tensor, torch.Tensor]], Optional[List[torch.Tensor]]][source]¶

Cross Validation¶

class ax.modelbridge.cross_validation.CVResult(observed: ax.core.observation.Observation, predicted: ax.core.observation.ObservationData)[source]¶

Bases: tuple

Container for cross validation results.

property observed¶

Alias for field number 0

property predicted¶

Alias for field number 1

ax.modelbridge.cross_validation.assess_model_fit(diagnostics: Dict[str, Dict[str, float]], significance_level: float = 0.1) → Tuple[Dict[str, float], Dict[str, float]][source]¶

Assess model fit for given diagnostics results.

It determines if a model fit is good or bad based on Fisher exact test p

Parameters

diagnostics – Output of compute_diagnostics

Returns

Two dictionaries, one for good metrics, one for bad metrics, each mapping metric name to p-value

ax.modelbridge.cross_validation.compute_diagnostics(result: List[ax.modelbridge.cross_validation.CVResult]) → Dict[str, Dict[str, float]][source]¶

Computes diagnostics for given cross validation results.

It provides a dictionary with values for the following diagnostics, for each metric:

• ‘Mean prediction CI’: the average width of the CIs at each of the CV predictions, relative to the observed mean.

• ‘MAPE’: mean absolute percentage error of the estimated mean relative to the observed mean.

• ‘Total raw effect’: the percent change from the smallest observed mean to the largest observed mean.

• ‘Correlation coefficient’: the Pearson correlation of the estimated and observed means.

• ‘Rank correlation’: the Spearman correlation of the estimated and observed means.

• ‘Fisher exact test p’: we test if the model is able to distinguish the bottom half of the observations from the top half, using Fisher’s exact test and the observed/estimated means. A low p value indicates that the model has some ability to identify good arms. A high p value indicates that the model cannot identify arms better than chance, or that the observations are too noisy to be able to tell.

Each of these is returned as a dictionary from metric name to value for that metric.

Parameters

result – Output of cross_validate

Returns

A dictionary keyed by diagnostic name with results as described above.

ax.modelbridge.cross_validation.cross_validate(model: ax.modelbridge.base.ModelBridge, folds: int = - 1, test_selector: Optional[Callable] = None) → List[ax.modelbridge.cross_validation.CVResult][source]¶

Cross validation for model predictions.

Splits the model’s training data into train/test folds and makes out-of-sample predictions on the test folds.

Train/test splits are made based on arm names, so that repeated observations of a arm will always be in the train or test set together.

The test set can be limited to a specific set of observations by passing in a test_selector callable. This function should take in an Observation and return a boolean indiciating if it should be used in the test set or not. For example, we can limit the test set to arms with trial 0 with test_selector = lambda obs: obs.features.trial_index == 0 If not provided, all observations will be available for the test set.

Parameters

• model – Fitted model (ModelBridge) to cross validate.

• folds – Number of folds. Use -1 for leave-one-out, otherwise will be k-fold.

• test_selector – Function for selecting observations for the test set.

Returns

A CVResult for each observation in the training data.

ax.modelbridge.cross_validation.cross_validate_by_trial(model: ax.modelbridge.base.ModelBridge, trial: int = - 1) → List[ax.modelbridge.cross_validation.CVResult][source]¶

Cross validation for model predictions on a particular trial.

Uses all of the data up until the specified trial to predict each of the arms that was launched in that trial. Defaults to the last trial.

Parameters

• model – Fitted model (ModelBridge) to cross validate.

• trial – Trial for which predictions are evaluated.

Returns

A CVResult for each observation in the training data.

Dispatch Utilities¶

ax.modelbridge.dispatch_utils.choose_generation_strategy(search_space: ax.core.search_space.SearchSpace, use_batch_trials: bool = False, enforce_sequential_optimization: bool = True, random_seed: Optional[int] = None, winsorize_botorch_model: bool = False, winsorization_limits: Optional[Tuple[Optional[float], Optional[float]]] = None, no_bayesian_optimization: bool = False, num_trials: Optional[int] = None, num_initialization_trials: Optional[int] = None, max_parallelism_cap: Optional[int] = None, max_parallelism_override: Optional[int] = None) → ax.modelbridge.generation_strategy.GenerationStrategy[source]¶

Select an appropriate generation strategy based on the properties of the search space and expected settings of the experiment, such as number of arms per trial, optimization algorithm settings, expected number of trials in the experiment, etc.

Parameters

• search_space – SearchSpace, based on the properties of which to select the generation strategy.

• use_batch_trials – Whether this generation strategy will be used to generate batched trials instead of 1-arm trials.

• enforce_sequential_optimization – Whether to enforce that 1) the generation strategy needs to be updated with min_trials_observed observations for a given generation step before proceeding to the next one and 2) maximum number of trials running at once (max_parallelism) if enforced for the BayesOpt step. NOTE: max_parallelism_override and max_parallelism_cap settings will still take their effect on max parallelism even if enforce_sequential_optimization=False, so if those settings are specified, max parallelism will be enforced.

• random_seed – Fixed random seed for the Sobol generator.

• winsorize_botorch_model – Whether to apply the winsorization transform prior to applying other transforms for fitting the BoTorch model.

• winsorization_limits – Bounds for winsorization, if winsorizing, expressed as percentile. Usually only the upper winsorization trim is used when minimizing, and only the lower when maximizing.

• no_bayesian_optimization – If True, Bayesian optimization generation strategy will not be suggested and quasi-random strategy will be used.

• num_trials – Total number of trials in the optimization, if known in advance.

• num_initialization_trials – Specific number of initialization trials, if wanted. Typically, initialization trials are generated quasi-randomly.

• max_parallelism_override – Integer, with which to override the default max parallelism setting for all steps in the generation strategy returned from this function. Each generation step has a max_parallelism value, which restricts how many trials can run simultaneously during a given generation step. By default, the parallelism setting is chosen as appropriate for the model in a given generation step. If max_parallelism_override is -1, no max parallelism will be enforced for any step of the generation strategy. Be aware that parallelism is limited to improve performance of Bayesian optimization, so only disable its limiting if necessary.

• max_parallelism_cap – Integer cap on parallelism in this generation strategy. If specified, max_parallelism setting in each generation step will be set to the minimum of the default setting for that step and the value of this cap. max_parallelism_cap is meant to just be a hard limit on parallelism (e.g. to avoid overloading machine(s) that evaluate the experiment trials). Specify only if not specifying max_parallelism_override.

ax.modelbridge.transforms.base¶

class ax.modelbridge.transforms.base.Transform(search_space: Optional[ax.core.search_space.SearchSpace], observation_features: List[ax.core.observation.ObservationFeatures], observation_data: List[ax.core.observation.ObservationData], config: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None)[source]¶

Bases: object

Defines the API for a transform that is applied to search_space, observation_features, observation_data, and optimization_config.

Transforms are used to adapt the search space and data into the types and structures expected by the model. When Transforms are used (for instance, in ModelBridge), it is always assumed that they may potentially mutate the transformed object in-place.

Forward transforms are defined for all four of those quantities. Reverse transforms are defined for observation_data and observation.

The forward transform for observation features must accept a partial observation with not all features recorded.

Forward and reverse transforms for observation data accept a list of observation features as an input, but they will not be mutated.

The forward transform for optimization config accepts the modelbridge and fixed features as inputs, but they will not be mutated.

This class provides an identify transform.

config: Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]¶

transform_observation_data(observation_data: List[ax.core.observation.ObservationData], observation_features: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationData][source]¶

Transform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

This takes in observation_features, so that data transforms can be conditional on features, but observation_features are notmutated.

Parameters

• observation_data – Observation data

• observation_features – Corresponding observation features

Returns: transformed observation data

transform_observation_features(observation_features: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationFeatures][source]¶

Transform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

observation_features – Observation features

Returns: transformed observation features

transform_optimization_config(optimization_config: ax.core.optimization_config.OptimizationConfig, modelbridge: Optional[modelbridge_module.base.ModelBridge], fixed_features: ax.core.observation.ObservationFeatures) → ax.core.optimization_config.OptimizationConfig[source]¶

Transform optimization config.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

optimization_config – The optimization config

Returns: transformed optimization config.

transform_search_space(search_space: ax.core.search_space.SearchSpace) → ax.core.search_space.SearchSpace[source]¶

Transform search space.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

search_space – The search space

Returns: transformed search space.

untransform_observation_data(observation_data: List[ax.core.observation.ObservationData], observation_features: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationData][source]¶

Untransform observation data.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

• observation_data – Observation data, in transformed space

• observation_features – Corresponding observation features, in same space.

Returns: observation data in original space.

untransform_observation_features(observation_features: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationFeatures][source]¶

Untransform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

observation_features – Observation features in the transformed space

Returns: observation features in the original space

ax.modelbridge.transforms.cast¶

class ax.modelbridge.transforms.cast.Cast(search_space: ax.core.search_space.SearchSpace, observation_features: Optional[List[ax.core.observation.ObservationFeatures]] = None, observation_data: Optional[List[ax.core.observation.ObservationData]] = None, config: Optional[Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]] = None)[source]¶

Bases: ax.modelbridge.transforms.base.Transform

Cast each param value to the respective parameter’s type/format

This is a default transform that should run across all models

Transform is done in-place.

config: Dict[str, Union[int, float, str, botorch.acquisition.acquisition.AcquisitionFunction, Dict[str, Any]]]¶

untransform_observation_features(observation_features: List[ax.core.observation.ObservationFeatures]) → List[ax.core.observation.ObservationFeatures][source]¶

Untransform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters

observation_features – Observation features in the transformed space

Returns: observation features in the original space