ax.preview¶

Configs¶

class ax.preview.api.configs.ChoiceParameterConfig(name: str, values: List[float] | List[int] | List[str] | List[bool], parameter_type: ParameterType, is_ordered: bool | None = None, dependent_parameters: dict[float | int | str | bool, str] | None = None)[source]¶

Bases: object

ChoiceParameterConfig allows users to specify the a discrete dimension of an experiment’s search space and will internally validate the inputs.

dependent_parameters: dict[float | int | str | bool, str] | None = None¶

is_ordered: bool | None = None¶

name: str¶

parameter_type: ParameterType¶

values: List[float] | List[int] | List[str] | List[bool]¶

class ax.preview.api.configs.DatabaseConfig(url: str)[source]¶

Bases: object

url: str¶

class ax.preview.api.configs.ExperimentConfig(name: str, parameters: list[~ax.preview.api.configs.RangeParameterConfig | ~ax.preview.api.configs.ChoiceParameterConfig], parameter_constraints: list[str] = <factory>, description: str | None = None, owner: str | None = None)[source]¶

Bases: object

ExperimentConfig allows users to specify the SearchSpace and OptimizationConfig of an Experiment and validates their inputs jointly.

This will also be the construct that handles transforming string-based inputs (the objective, parameter constraints, and output constraints) into their corresponding Ax class using SymPy.

description: str | None = None¶

name: str¶

owner: str | None = None¶

parameter_constraints: list[str]¶

parameters: list[RangeParameterConfig | ChoiceParameterConfig]¶

class ax.preview.api.configs.GenerationStrategyConfig(num_trials: int | None = None, num_initialization_trials: int | None = None, maximum_parallelism: int | None = None)[source]¶

Bases: object

maximum_parallelism: int | None = None¶

num_initialization_trials: int | None = None¶

num_trials: int | None = None¶

class ax.preview.api.configs.OrchestrationConfig(parallelism: int = 1, tolerated_trial_failure_rate: float = 0.5, seconds_between_polls: float = 1.0)[source]¶

Bases: object

parallelism: int = 1¶

seconds_between_polls: float = 1.0¶

tolerated_trial_failure_rate: float = 0.5¶

class ax.preview.api.configs.ParameterScaling(value)[source]¶

Bases: Enum

The ParameterScaling enum allows users to specify which scaling to apply during candidate generation. This is useful for parameters that should not be explored on the same scale, such as learning rates and batch sizes.

LINEAR = 'linear'¶

LOG = 'log'¶

class ax.preview.api.configs.ParameterType(value)[source]¶

Bases: Enum

The ParameterType enum allows users to specify the type of a parameter.

BOOL = 'bool'¶

FLOAT = 'float'¶

INT = 'int'¶

STRING = 'str'¶

class ax.preview.api.configs.RangeParameterConfig(name: str, bounds: tuple[float, float], parameter_type: ParameterType, step_size: float | None = None, scaling: ParameterScaling | None = None)[source]¶

Bases: object

RangeParameterConfig allows users to specify the a continuous dimension of an experiment’s search space and will internally validate the inputs.

bounds: tuple[float, float]¶

name: str¶

parameter_type: ParameterType¶

scaling: ParameterScaling | None = None¶

step_size: float | None = None¶