ax.storage

JSON

ax.storage.json_store.decoder module

ax.storage.json_store.decoder.ax_class_from_json_dict(_class: Type, object_json: Dict[str, Any])Any[source]

Reinstantiates an Ax class registered in DECODER_REGISTRY from a JSON dict.

ax.storage.json_store.decoder.data_from_json(data_by_trial_json: Dict[str, Any])Dict[int, OrderedDict[int, Data]][source]

Load Ax Data from JSON.

ax.storage.json_store.decoder.experiment_from_json(object_json: Dict[str, Any])ax.core.experiment.Experiment[source]

Load Ax Experiment from JSON.

ax.storage.json_store.decoder.generation_step_from_json(generation_step_json: Dict[str, Any])ax.modelbridge.generation_node.GenerationStep[source]

Load generation step from JSON.

ax.storage.json_store.decoder.generation_strategy_from_json(generation_strategy_json: Dict[str, Any], experiment: Optional[ax.core.experiment.Experiment] = None)ax.modelbridge.generation_strategy.GenerationStrategy[source]

Load generation strategy from JSON.

ax.storage.json_store.decoder.generator_run_from_json(object_json: Dict[str, Any])ax.core.generator_run.GeneratorRun[source]

Load Ax GeneratorRun from JSON.

ax.storage.json_store.decoder.multi_type_experiment_from_json(object_json: Dict[str, Any])ax.core.multi_type_experiment.MultiTypeExperiment[source]

Load AE MultiTypeExperiment from JSON.

ax.storage.json_store.decoder.object_from_json(object_json: Any)Any[source]

Recursively load objects from a JSON-serializable dictionary.

ax.storage.json_store.decoder.parameter_constraints_from_json(parameter_constraint_json: List[Dict[str, Any]], parameters: List[ax.core.parameter.Parameter])List[ax.core.parameter_constraint.ParameterConstraint][source]

Load ParameterConstraints from JSON.

Order and SumConstraint are tied to a search space, and require that SearchSpace’s parameters to be passed in for decoding.

Parameters
  • parameter_constraint_json – JSON representation of parameter constraints.

  • parameters – Parameter definitions for decoding via parameter names.

Returns

Python classes for parameter constraints.

Return type

parameter_constraints

ax.storage.json_store.decoder.search_space_from_json(search_space_json: Dict[str, Any])ax.core.search_space.SearchSpace[source]

Load a SearchSpace from JSON.

This function is necessary due to the coupled loading of SearchSpace and parameter constraints.

ax.storage.json_store.decoder.simple_benchmark_problem_from_json(object_json: Dict[str, Any])ax.benchmark.benchmark_problem.SimpleBenchmarkProblem[source]

Load a benchmark problem from JSON.

ax.storage.json_store.decoder.simple_experiment_from_json(object_json: Dict[str, Any])ax.core.simple_experiment.SimpleExperiment[source]

Load AE SimpleExperiment from JSON.

ax.storage.json_store.decoder.trials_from_json(experiment: ax.core.experiment.Experiment, trials_json: Dict[str, Any])Dict[int, ax.core.base_trial.BaseTrial][source]

Load Ax Trials from JSON.

ax.storage.json_store.decoders module

ax.storage.json_store.decoders.batch_trial_from_json(experiment: core.experiment.Experiment, index: int, trial_type: Optional[str], status: TrialStatus, time_created: datetime, time_completed: Optional[datetime], time_staged: Optional[datetime], time_run_started: Optional[datetime], abandoned_reason: Optional[str], run_metadata: Optional[Dict[str, Any]], generator_run_structs: List[GeneratorRunStruct], runner: Optional[Runner], abandoned_arms_metadata: Dict[str, AbandonedArm], num_arms_created: int, status_quo: Optional[Arm], status_quo_weight_override: float, optimize_for_power: Optional[bool], ttl_seconds: Optional[int] = None, generation_step_index: Optional[int] = None, properties: Optional[Dict[str, Any]] = None, stop_metadata: Optional[Dict[str, Any]] = None, **kwargs: Any)BatchTrial[source]

Load Ax BatchTrial from JSON.

Other classes don’t need explicit deserializers, because we can just use their constructors (see decoder.py). However, the constructor for Batch does not allow us to exactly recreate an existing object.

ax.storage.json_store.decoders.class_from_json(json: Dict[str, Any])Type[Any][source]

Load any class registered in CLASS_DECODER_REGISTRY from JSON.

ax.storage.json_store.decoders.transform_type_from_json(object_json: Dict[str, Any])Type[ax.modelbridge.transforms.base.Transform][source]

Load the transform type from JSON.

ax.storage.json_store.decoders.trial_from_json(experiment: core.experiment.Experiment, index: int, trial_type: Optional[str], status: TrialStatus, time_created: datetime, time_completed: Optional[datetime], time_staged: Optional[datetime], time_run_started: Optional[datetime], abandoned_reason: Optional[str], run_metadata: Optional[Dict[str, Any]], generator_run: GeneratorRun, runner: Optional[Runner], num_arms_created: int, ttl_seconds: Optional[int] = None, generation_step_index: Optional[int] = None, properties: Optional[Dict[str, Any]] = None, stop_metadata: Optional[Dict[str, Any]] = None, **kwargs: Any)Trial[source]

Load Ax trial from JSON.

Other classes don’t need explicit deserializers, because we can just use their constructors (see decoder.py). However, the constructor for Trial does not allow us to exactly recreate an existing object.

ax.storage.json_store.encoder module

ax.storage.json_store.encoder.object_to_json(obj: Any)Any[source]

Convert an Ax object to a JSON-serializable dictionary.

The root node passed to this function should always be an instance of a core Ax class or a JSON-compatible python builtin. The sub-fields of the input will then be recursively passed to this function.

e.g. if we pass an instance of Experiment, we will first fall through to the line object_dict = ENCODER_REGISTRY[_type](object), which will convert the Experiment to a (shallow) dictionary, where search subfield remains “unconverted”, i.e.: {“name”: <name: string>, “search_space”: <search space: SearchSpace>}. We then pass each item of the dictionary back into this function to recursively convert the entire object.

ax.storage.json_store.encoders module

ax.storage.json_store.encoders.arm_to_dict(arm: ax.core.arm.Arm)Dict[str, Any][source]

Convert Ax arm to a dictionary.

ax.storage.json_store.encoders.batch_to_dict(batch: ax.core.batch_trial.BatchTrial)Dict[str, Any][source]

Convert Ax batch to a dictionary.

ax.storage.json_store.encoders.benchmark_problem_to_dict(benchmark_problem: ax.benchmark.benchmark_problem.BenchmarkProblem)Dict[str, Any][source]

Converts an Ax benchmark problem to a serializable dictionary.

ax.storage.json_store.encoders.botorch_model_to_dict(model: ax.models.torch.botorch_modular.model.BoTorchModel)Dict[str, Any][source]

Convert Ax model to a dictionary.

ax.storage.json_store.encoders.botorch_modular_to_dict(class_type: Type[Any])Dict[str, Any][source]

Convert any class to a dictionary.

ax.storage.json_store.encoders.choice_parameter_to_dict(parameter: ax.core.parameter.ChoiceParameter)Dict[str, Any][source]

Convert Ax choice parameter to a dictionary.

ax.storage.json_store.encoders.data_to_dict(data: ax.core.data.Data)Dict[str, Any][source]

Convert Ax data to a dictionary.

ax.storage.json_store.encoders.experiment_to_dict(experiment: ax.core.experiment.Experiment)Dict[str, Any][source]

Convert Ax experiment to a dictionary.

ax.storage.json_store.encoders.fixed_parameter_to_dict(parameter: ax.core.parameter.FixedParameter)Dict[str, Any][source]

Convert Ax fixed parameter to a dictionary.

ax.storage.json_store.encoders.generation_step_to_dict(generation_step: ax.modelbridge.generation_node.GenerationStep)Dict[str, Any][source]

Converts Ax generation step to a dictionary.

ax.storage.json_store.encoders.generation_strategy_to_dict(generation_strategy: ax.modelbridge.generation_strategy.GenerationStrategy)Dict[str, Any][source]

Converts Ax generation strategy to a dictionary.

ax.storage.json_store.encoders.generator_run_to_dict(generator_run: ax.core.generator_run.GeneratorRun)Dict[str, Any][source]

Convert Ax generator run to a dictionary.

ax.storage.json_store.encoders.map_data_to_dict(map_data: ax.core.map_data.MapData)Dict[str, Any][source]

Convert Ax map data to a dictionary.

ax.storage.json_store.encoders.map_key_info_to_dict(mki: ax.core.map_data.MapKeyInfo)Dict[str, Any][source]

Convert Ax map data metadata to a dictionary.

ax.storage.json_store.encoders.metric_to_dict(metric: ax.core.metric.Metric)Dict[str, Any][source]

Convert Ax metric to a dictionary.

ax.storage.json_store.encoders.multi_objective_optimization_config_to_dict(multi_objective_optimization_config: ax.core.optimization_config.MultiObjectiveOptimizationConfig)Dict[str, Any][source]

Convert Ax optimization config to a dictionary.

ax.storage.json_store.encoders.multi_objective_to_dict(objective: ax.core.objective.MultiObjective)Dict[str, Any][source]

Convert Ax objective to a dictionary.

ax.storage.json_store.encoders.multi_type_experiment_to_dict(experiment: ax.core.multi_type_experiment.MultiTypeExperiment)Dict[str, Any][source]

Convert AE multitype experiment to a dictionary.

ax.storage.json_store.encoders.objective_to_dict(objective: ax.core.objective.Objective)Dict[str, Any][source]

Convert Ax objective to a dictionary.

ax.storage.json_store.encoders.observation_features_to_dict(obs_features: ax.core.observation.ObservationFeatures)Dict[str, Any][source]

Converts Ax observation features to a dictionary

ax.storage.json_store.encoders.optimization_config_to_dict(optimization_config: ax.core.optimization_config.OptimizationConfig)Dict[str, Any][source]

Convert Ax optimization config to a dictionary.

ax.storage.json_store.encoders.order_parameter_constraint_to_dict(parameter_constraint: ax.core.parameter_constraint.OrderConstraint)Dict[str, Any][source]

Convert Ax order parameter constraint to a dictionary.

ax.storage.json_store.encoders.outcome_constraint_to_dict(outcome_constraint: ax.core.outcome_constraint.OutcomeConstraint)Dict[str, Any][source]

Convert Ax outcome constraint to a dictionary.

ax.storage.json_store.encoders.parameter_constraint_to_dict(parameter_constraint: ax.core.parameter_constraint.ParameterConstraint)Dict[str, Any][source]

Convert Ax sum parameter constraint to a dictionary.

ax.storage.json_store.encoders.percentile_early_stopping_strategy_to_dict(strategy: ax.early_stopping.strategies.PercentileEarlyStoppingStrategy)Dict[str, Any][source]

Convert Ax percentile early stopping strategy to a dictionary.

ax.storage.json_store.encoders.range_parameter_to_dict(parameter: ax.core.parameter.RangeParameter)Dict[str, Any][source]

Convert Ax range parameter to a dictionary.

ax.storage.json_store.encoders.runner_to_dict(runner: ax.runners.synthetic.SyntheticRunner)Dict[str, Any][source]

Convert Ax synthetic runner to a dictionary.

ax.storage.json_store.encoders.scalarized_objective_to_dict(objective: ax.core.objective.ScalarizedObjective)Dict[str, Any][source]

Convert Ax objective to a dictionary.

ax.storage.json_store.encoders.search_space_to_dict(search_space: ax.core.search_space.SearchSpace)Dict[str, Any][source]

Convert Ax search space to a dictionary.

ax.storage.json_store.encoders.simple_experiment_to_dict(experiment: ax.core.simple_experiment.SimpleExperiment)Dict[str, Any][source]

Convert AE simple experiment to a dictionary.

ax.storage.json_store.encoders.sum_parameter_constraint_to_dict(parameter_constraint: ax.core.parameter_constraint.SumConstraint)Dict[str, Any][source]

Convert Ax sum parameter constraint to a dictionary.

ax.storage.json_store.encoders.surrogate_to_dict(surrogate: ax.models.torch.botorch_modular.surrogate.Surrogate)Dict[str, Any][source]

Convert Ax surrogate to a dictionary.

ax.storage.json_store.encoders.transform_type_to_dict(transform_type: Type[ax.modelbridge.transforms.base.Transform])Dict[str, Any][source]

Convert a transform class to a dictionary.

ax.storage.json_store.encoders.trial_to_dict(trial: ax.core.trial.Trial)Dict[str, Any][source]

Convert Ax trial to a dictionary.

ax.storage.json_store.encoders.winsorization_config_to_dict(config: ax.modelbridge.transforms.winsorize.WinsorizationConfig)Dict[str, Any][source]

Convert Ax winsorization config to a dictionary.

ax.storage.json_store.load module

ax.storage.json_store.load.load_experiment(filepath: str)ax.core.experiment.Experiment[source]

Load experiment from file.

  1. Read file.

  2. Convert dictionary to Ax experiment instance.

ax.storage.json_store.registry module

ax.storage.json_store.save module

ax.storage.json_store.save.save_experiment(experiment: ax.core.experiment.Experiment, filepath: str)None[source]

Save experiment to file.

  1. Convert Ax experiment to JSON-serializable dictionary.

  2. Write to file.

SQLAlchemy (MySQL / SQLite)

ax.storage.sqa_store.base_decoder module

ax.storage.sqa_store.base_encoder module

ax.storage.sqa_store.db module

class ax.storage.sqa_store.db.SQABase[source]

Bases: object

Metaclass for SQLAlchemy classes corresponding to core Ax classes.

ax.storage.sqa_store.db.create_all_tables(engine: sqlalchemy.engine.base.Engine)None[source]

Create all tables that inherit from Base.

Parameters

engine – a SQLAlchemy engine with a connection to a MySQL or SQLite DB.

Note

In order for all tables to be correctly created, all modules that define a mapped class that inherits from Base must be imported.

ax.storage.sqa_store.db.create_mysql_engine_from_creator(creator: Callable, echo: bool = False, pool_recycle: int = 10, **kwargs: Any)sqlalchemy.engine.base.Engine[source]

Create a SQLAlchemy engine with the MySQL dialect given a creator function.

Parameters
  • creator – a callable which returns a DBAPI connection.

  • echo – if True, set engine to be verbose.

  • pool_recycle – number of seconds after which to recycle connections. -1 means no timeout. Default is 10 seconds.

  • **kwargs – keyword args passed to create_engine

Returns

SQLAlchemy engine with connection to MySQL DB.

Return type

Engine

ax.storage.sqa_store.db.create_mysql_engine_from_url(url: str, echo: bool = False, pool_recycle: int = 10, **kwargs: Any)sqlalchemy.engine.base.Engine[source]

Create a SQLAlchemy engine with the MySQL dialect given a database url.

Parameters
  • url – a database url that can include username, password, hostname, database name as well as optional keyword arguments for additional configuration. e.g. dialect+driver://username:[email protected]:port/database.

  • echo – if True, set engine to be verbose.

  • pool_recycle – number of seconds after which to recycle connections. -1 means no timeout. Default is 10 seconds.

  • **kwargs – keyword args passed to create_engine

Returns

SQLAlchemy engine with connection to MySQL DB.

Return type

Engine

ax.storage.sqa_store.db.create_test_engine(path: Optional[str] = None, echo: bool = True)sqlalchemy.engine.base.Engine[source]

Creates a SQLAlchemy engine object for use in unit tests.

Parameters
  • path – if None, use in-memory SQLite; else attempt to create a SQLite DB in the path provided.

  • echo – if True, set engine to be verbose.

Returns

an instance of SQLAlchemy engine.

Return type

Engine

ax.storage.sqa_store.db.get_engine()sqlalchemy.engine.base.Engine[source]

Fetch a SQLAlchemy engine, if already initialized.

If not initialized, need to either call init_engine_and_session_factory or get_session explicitly.

Returns

an instance of a SQLAlchemy engine with a connection to a DB.

Return type

Engine

ax.storage.sqa_store.db.get_session()sqlalchemy.orm.session.Session[source]

Fetch a SQLAlchemy session with a connection to a DB.

Unless init_engine_and_session_factory is called first with custom args, this will automatically initialize a connection to xdb.adaptive_experiment.

Returns

an instance of a SQLAlchemy session.

Return type

Session

ax.storage.sqa_store.db.init_engine_and_session_factory(url: Optional[str] = None, creator: Optional[Callable] = None, echo: bool = False, force_init: bool = False, **kwargs: Any)None[source]

Initialize the global engine and SESSION_FACTORY for SQLAlchemy.

The initialization needs to only happen once. Note that it is possible to re-initialize the engine by setting the force_init flag to True, but this should only be used if you are absolutely certain that you know what you are doing.

Parameters
  • url – a database url that can include username, password, hostname, database name as well as optional keyword arguments for additional configuration. e.g. dialect+driver://username:[email protected]:port/database. Either this argument or creator argument must be specified.

  • creator – a callable which returns a DBAPI connection. Either this argument or url argument must be specified.

  • echo – if True, logging for engine is enabled.

  • force_init – if True, allows re-initializing engine and session factory.

  • **kwargs – keyword arguments passed to create_mysql_engine_from_creator

ax.storage.sqa_store.db.init_test_engine_and_session_factory(tier_or_path: Optional[str] = None, echo: bool = False, force_init: bool = False, **kwargs: Any)None[source]

Initialize the global engine and SESSION_FACTORY for SQLAlchemy, using an in-memory SQLite database.

The initialization needs to only happen once. Note that it is possible to re-initialize the engine by setting the force_init flag to True, but this should only be used if you are absolutely certain that you know what you are doing.

Parameters
  • tier_or_path – the name of the DB tier.

  • echo – if True, logging for engine is enabled.

  • force_init – if True, allows re-initializing engine and session factory.

  • **kwargs – keyword arguments passed to create_mysql_engine_from_creator

ax.storage.sqa_store.db.optional_session_scope(session: Optional[sqlalchemy.orm.session.Session] = None)AbstractContextManager[sqlalchemy.orm.session.Session][source]
ax.storage.sqa_store.db.remove_test_db_file(tier_or_path: str)None[source]

Remove the test DB file from system, useful for cleanup in tests.

ax.storage.sqa_store.db.session_scope()Generator[sqlalchemy.orm.session.Session, None, None][source]

Provide a transactional scope around a series of operations.

ax.storage.sqa_store.delete module

ax.storage.sqa_store.json module

class ax.storage.sqa_store.json.JSONEncodedMediumText(object_pairs_hook: Optional[Any] = None, *args: List[Any], **kwargs: Dict[Any, Any])[source]

Bases: ax.storage.sqa_store.json.JSONEncodedObject

Class for JSON-encoding objects in SQLAlchemy, backed by MEDIUMTEXT (MySQL).

See description in JSONEncodedObject.

impl: sqlalchemy.sql.sqltypes.VARCHAR = Text(length=16777215)
class ax.storage.sqa_store.json.JSONEncodedObject(object_pairs_hook: Optional[Any] = None, *args: List[Any], **kwargs: Dict[Any, Any])[source]

Bases: sqlalchemy.sql.type_api.TypeDecorator

Class for JSON-encoding objects in SQLAlchemy.

Represents an object that is automatically marshalled and unmarshalled to/from the corresponding JSON string. By itself, this data structure does not track any changes.

impl: sqlalchemy.sql.sqltypes.VARCHAR = VARCHAR(length=4096)
process_bind_param(value: Any, dialect: Any)Optional[str][source]

Receive a bound parameter value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for incoming data values. This method is called at statement execution time and is passed the literal Python data value which is to be associated with a bound parameter in the statement.

The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_result_value()

process_result_value(value: Any, dialect: Any)Any[source]

Receive a result-row column value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for data values being received in result rows coming from the database. This method is called at result fetching time and is passed the literal Python data value that’s extracted from a database result row.

The operation could be anything desired to perform custom behavior, such as transforming or deserializing data.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_bind_param()

class ax.storage.sqa_store.json.JSONEncodedText(object_pairs_hook: Optional[Any] = None, *args: List[Any], **kwargs: Dict[Any, Any])[source]

Bases: ax.storage.sqa_store.json.JSONEncodedObject

Class for JSON-encoding objects in SQLAlchemy, backed by TEXT (MySQL).

See description in JSONEncodedObject.

impl

alias of sqlalchemy.sql.sqltypes.Text

object_pairs_hook: Any

ax.storage.sqa_store.load module

ax.storage.sqa_store.save module

ax.storage.sqa_store.structs module

ax.storage.sqa_store.sqa_classes module

class ax.storage.sqa_store.sqa_classes.SQAAbandonedArm(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

abandoned_reason: Optional[str] = Column(None, String(length=255), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
name: str = Column(None, String(length=100), table=None, nullable=False)
time_abandoned: datetime.datetime = Column(None, IntTimestamp(), table=None, nullable=False, default=ColumnDefault(<function datetime.now>))
trial_id: int = Column(None, Integer(), ForeignKey('trial_v2.id'), table=None)
class ax.storage.sqa_store.sqa_classes.SQAArm(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

generator_run_id: int = Column(None, Integer(), ForeignKey('generator_run_v2.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
name: Optional[str] = Column(None, String(length=100), table=None)
parameters: Dict[str, Optional[Union[str, bool, float, int]]] = Column(None, JSONEncodedText(), table=None, nullable=False)
weight: float = Column(None, Float(), table=None, nullable=False, default=ColumnDefault(1.0))
class ax.storage.sqa_store.sqa_classes.SQAData(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

data_json: str = Column(None, Text(length=4294967295), table=None, nullable=False)
description: Optional[str] = Column(None, String(length=255), table=None)
experiment_id: int = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
generation_strategy_id: Optional[int] = Column(None, Integer(), ForeignKey('generation_strategy.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
structure_metadata_json: str = Column(None, Text(length=4294967295), table=None)
time_created: int = Column(None, BigInteger(), table=None, nullable=False)
trial_index: Optional[int] = Column(None, Integer(), table=None)
class ax.storage.sqa_store.sqa_classes.SQAExperiment(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

data: List[ax.storage.sqa_store.sqa_classes.SQAData] = <RelationshipProperty at 0x7f7ae31018c0; no key>
default_data_type: ax.core.experiment.DataType = Column(None, IntEnum(), table=None)
default_trial_type: Optional[str] = Column(None, String(length=100), table=None)
description: Optional[str] = Column(None, String(length=255), table=None)
experiment_type: Optional[int] = Column(None, Integer(), table=None)
generation_strategy: Optional[ax.storage.sqa_store.sqa_classes.SQAGenerationStrategy] = <RelationshipProperty at 0x7f7ae3101c20; no key>
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
is_test: bool = Column(None, Boolean(), table=None, nullable=False, default=ColumnDefault(False))
metrics: List[ax.storage.sqa_store.sqa_classes.SQAMetric] = <RelationshipProperty at 0x7f7ae3101950; no key>
name: str = Column(None, String(length=100), table=None, nullable=False)
parameter_constraints: List[ax.storage.sqa_store.sqa_classes.SQAParameterConstraint] = <RelationshipProperty at 0x7f7ae3101a70; no key>
parameters: List[ax.storage.sqa_store.sqa_classes.SQAParameter] = <RelationshipProperty at 0x7f7ae31019e0; no key>
properties: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None, default=ColumnDefault({}))
runners: List[ax.storage.sqa_store.sqa_classes.SQARunner] = <RelationshipProperty at 0x7f7ae3101b00; no key>
status_quo_name: Optional[str] = Column(None, String(length=100), table=None)
status_quo_parameters: Optional[Dict[str, Optional[Union[str, bool, float, int]]]] = Column(None, JSONEncodedText(), table=None)
time_created: datetime.datetime = Column(None, IntTimestamp(), table=None, nullable=False)
trials: List[ax.storage.sqa_store.sqa_classes.SQATrial] = <RelationshipProperty at 0x7f7ae3101b90; no key>
class ax.storage.sqa_store.sqa_classes.SQAGenerationStrategy(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

curr_index: int = Column(None, Integer(), table=None, nullable=False)
experiment_id: Optional[int] = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
generator_runs: List[ax.storage.sqa_store.sqa_classes.SQAGeneratorRun] = <RelationshipProperty at 0x7f7ae30ec830; no key>
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
name: str = Column(None, String(length=100), table=None, nullable=False)
steps: List[Dict[str, Any]] = Column(None, JSONEncodedObject(length=4096), table=None, nullable=False)
class ax.storage.sqa_store.sqa_classes.SQAGeneratorRun(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

arms: List[ax.storage.sqa_store.sqa_classes.SQAArm] = <RelationshipProperty at 0x7f7ae30ec200; no key>
best_arm_name: Optional[str] = Column(None, String(length=100), table=None)
best_arm_parameters: Optional[Dict[str, Optional[Union[str, bool, float, int]]]] = Column(None, JSONEncodedText(), table=None)
best_arm_predictions: Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str, float]]]]] = Column(None, JSONEncodedObject(length=4096), table=None)
bridge_kwargs: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
candidate_metadata_by_arm_signature: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
fit_time: Optional[float] = Column(None, Float(), table=None)
gen_metadata: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
gen_time: Optional[float] = Column(None, Float(), table=None)
generation_step_index: Optional[int] = Column(None, Integer(), table=None)
generation_strategy_id: Optional[int] = Column(None, Integer(), ForeignKey('generation_strategy.id'), table=None)
generator_run_type: Optional[int] = Column(None, Integer(), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
index: Optional[int] = Column(None, Integer(), table=None)
metrics: List[ax.storage.sqa_store.sqa_classes.SQAMetric] = <RelationshipProperty at 0x7f7ae30d84d0; no key>
model_key: Optional[str] = Column(None, String(length=100), table=None)
model_kwargs: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
model_predictions: Optional[Tuple[Dict[str, List[float]], Dict[str, Dict[str, List[float]]]]] = Column(None, JSONEncodedObject(length=4096), table=None)
model_state_after_gen: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
parameter_constraints: List[ax.storage.sqa_store.sqa_classes.SQAParameterConstraint] = <RelationshipProperty at 0x7f7ae30ec560; no key>
parameters: List[ax.storage.sqa_store.sqa_classes.SQAParameter] = <RelationshipProperty at 0x7f7ae30ec4d0; no key>
time_created: datetime.datetime = Column(None, IntTimestamp(), table=None, nullable=False, default=ColumnDefault(<function datetime.now>))
trial_id: Optional[int] = Column(None, Integer(), ForeignKey('trial_v2.id'), table=None)
weight: Optional[float] = Column(None, Float(), table=None)
class ax.storage.sqa_store.sqa_classes.SQAMetric(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

bound: Optional[float] = Column(None, Float(), table=None)
canonical_name: Optional[str] = Column(None, String(length=100), table=None)
experiment_id: Optional[int] = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
generator_run_id: Optional[int] = Column(None, Integer(), ForeignKey('generator_run_v2.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
intent: ax.storage.utils.MetricIntent = Column(None, StringEnum(length=100), table=None, nullable=False)
lower_is_better: Optional[bool] = Column(None, Boolean(), table=None)
metric_type: int = Column(None, Integer(), table=None, nullable=False)
minimize: Optional[bool] = Column(None, Boolean(), table=None)
name: str = Column(None, String(length=255), table=None, nullable=False)
op: Optional[ax.core.types.ComparisonOp] = Column(None, IntEnum(), table=None)
properties: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None, default=ColumnDefault({}))
relative: Optional[bool] = Column(None, Boolean(), table=None)
scalarized_objective_children_metrics = <RelationshipProperty at 0x7f7ae30d88c0; no key>
scalarized_objective_id = Column(None, Integer(), ForeignKey('metric_v2.id'), table=None)
scalarized_objective_weight: Optional[float] = Column(None, Float(), table=None)
scalarized_outcome_constraint_children_metrics = <RelationshipProperty at 0x7f7ae30d8a70; no key>
scalarized_outcome_constraint_id = Column(None, Integer(), ForeignKey('metric_v2.id'), table=None)
scalarized_outcome_constraint_weight: Optional[float] = Column(None, Float(), table=None)
trial_type: Optional[str] = Column(None, String(length=100), table=None)
class ax.storage.sqa_store.sqa_classes.SQAParameter(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

choice_values: Optional[List[Optional[Union[str, bool, float, int]]]] = Column(None, JSONEncodedObject(length=4096), table=None)
digits: Optional[int] = Column(None, Integer(), table=None)
domain_type: ax.storage.utils.DomainType = Column(None, IntEnum(), table=None, nullable=False)
experiment_id: Optional[int] = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
fixed_value: Optional[Union[str, bool, float, int]] = Column(None, JSONEncodedObject(length=4096), table=None)
generator_run_id: Optional[int] = Column(None, Integer(), ForeignKey('generator_run_v2.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
is_fidelity: Optional[bool] = Column(None, Boolean(), table=None)
is_ordered: Optional[bool] = Column(None, Boolean(), table=None)
is_task: Optional[bool] = Column(None, Boolean(), table=None)
log_scale: Optional[bool] = Column(None, Boolean(), table=None)
lower: Optional[float] = Column(None, Float(), table=None)
name: str = Column(None, String(length=100), table=None, nullable=False)
parameter_type: ax.core.parameter.ParameterType = Column(None, IntEnum(), table=None, nullable=False)
target_value: Optional[Union[str, bool, float, int]] = Column(None, JSONEncodedObject(length=4096), table=None)
upper: Optional[float] = Column(None, Float(), table=None)
class ax.storage.sqa_store.sqa_classes.SQAParameterConstraint(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

bound: float = Column(None, Float(), table=None, nullable=False)
constraint_dict: Dict[str, float] = Column(None, JSONEncodedObject(length=4096), table=None, nullable=False)
experiment_id: Optional[int] = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
generator_run_id: Optional[int] = Column(None, Integer(), ForeignKey('generator_run_v2.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
type: ax.storage.sqa_store.sqa_enum.IntEnum = Column(None, IntEnum(), table=None, nullable=False)
class ax.storage.sqa_store.sqa_classes.SQARunner(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

experiment_id: Optional[int] = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
properties: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None, default=ColumnDefault({}))
runner_type: int = Column(None, Integer(), table=None, nullable=False)
trial_id: Optional[int] = Column(None, Integer(), ForeignKey('trial_v2.id'), table=None)
trial_type: Optional[str] = Column(None, String(length=100), table=None)
class ax.storage.sqa_store.sqa_classes.SQATrial(*args: Any, **kwargs: Any)[source]

Bases: sqlalchemy.ext.declarative.

abandoned_arms: List[ax.storage.sqa_store.sqa_classes.SQAAbandonedArm] = <RelationshipProperty at 0x7f7ae31013b0; no key>
abandoned_reason: Optional[str] = Column(None, String(length=100), table=None)
deployed_name: Optional[str] = Column(None, String(length=100), table=None)
experiment_id: int = Column(None, Integer(), ForeignKey('experiment_v2.id'), table=None)
generation_step_index: Optional[int] = Column(None, Integer(), table=None)
generator_runs: List[ax.storage.sqa_store.sqa_classes.SQAGeneratorRun] = <RelationshipProperty at 0x7f7ae3101440; no key>
id: int = Column(None, Integer(), table=None, primary_key=True, nullable=False)
index: int = Column(None, Integer(), table=None, nullable=False)
is_batch: bool = Column('is_batched', Boolean(), table=None, nullable=False, default=ColumnDefault(True))
num_arms_created: int = Column(None, Integer(), table=None, nullable=False, default=ColumnDefault(0))
optimize_for_power: Optional[bool] = Column(None, Boolean(), table=None)
properties: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None, default=ColumnDefault({}))
run_metadata: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
runner: ax.storage.sqa_store.sqa_classes.SQARunner = <RelationshipProperty at 0x7f7ae31014d0; no key>
status: ax.core.base_trial.TrialStatus = Column(None, IntEnum(), table=None, nullable=False, default=ColumnDefault(<TrialStatus.CANDIDATE: 0>))
status_quo_name: Optional[str] = Column(None, String(length=100), table=None)
stop_metadata: Optional[Dict[str, Any]] = Column(None, JSONEncodedText(), table=None)
time_completed: Optional[datetime.datetime] = Column(None, IntTimestamp(), table=None)
time_created: datetime.datetime = Column(None, IntTimestamp(), table=None, nullable=False)
time_run_started: Optional[datetime.datetime] = Column(None, IntTimestamp(), table=None)
time_staged: Optional[datetime.datetime] = Column(None, IntTimestamp(), table=None)
trial_type: Optional[str] = Column(None, String(length=100), table=None)
ttl_seconds: Optional[int] = Column(None, Integer(), table=None)

ax.storage.sqa_store.sqa_config module

ax.storage.sqa_store.sqa_enum module

class ax.storage.sqa_store.sqa_enum.BaseNullableEnum(enum: Any, *arg: List[Any], **kw: Dict[Any, Any])[source]

Bases: sqlalchemy.sql.type_api.TypeDecorator

process_bind_param(value: Any, dialect: Any)Any[source]

Receive a bound parameter value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for incoming data values. This method is called at statement execution time and is passed the literal Python data value which is to be associated with a bound parameter in the statement.

The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_result_value()

process_result_value(value: Any, dialect: Any)Any[source]

Receive a result-row column value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for data values being received in result rows coming from the database. This method is called at result fetching time and is passed the literal Python data value that’s extracted from a database result row.

The operation could be anything desired to perform custom behavior, such as transforming or deserializing data.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_bind_param()

class ax.storage.sqa_store.sqa_enum.IntEnum(enum: Any, *arg: List[Any], **kw: Dict[Any, Any])[source]

Bases: ax.storage.sqa_store.sqa_enum.BaseNullableEnum

impl

alias of sqlalchemy.sql.sqltypes.SmallInteger

class ax.storage.sqa_store.sqa_enum.StringEnum(enum: Any, *arg: List[Any], **kw: Dict[Any, Any])[source]

Bases: ax.storage.sqa_store.sqa_enum.BaseNullableEnum

impl = VARCHAR(length=100)

ax.storage.sqa_store.timestamp module

class ax.storage.sqa_store.timestamp.IntTimestamp(*args, **kwargs)[source]

Bases: sqlalchemy.sql.type_api.TypeDecorator

impl

alias of sqlalchemy.sql.sqltypes.Integer

process_bind_param(value: Optional[datetime.datetime], dialect: sqlalchemy.engine.interfaces.Dialect)Optional[int][source]

Receive a bound parameter value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for incoming data values. This method is called at statement execution time and is passed the literal Python data value which is to be associated with a bound parameter in the statement.

The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_result_value()

process_result_value(value: Optional[int], dialect: sqlalchemy.engine.interfaces.Dialect)Optional[datetime.datetime][source]

Receive a result-row column value to be converted.

Custom subclasses of _types.TypeDecorator should override this method to provide custom behaviors for data values being received in result rows coming from the database. This method is called at result fetching time and is passed the literal Python data value that’s extracted from a database result row.

The operation could be anything desired to perform custom behavior, such as transforming or deserializing data.

Parameters
  • value – Data to operate upon, of any type expected by this method in the subclass. Can be None.

  • dialect – the Dialect in use.

See also

types_typedecorator

_types.TypeDecorator.process_bind_param()

ax.storage.sqa_store.utils module

ax.storage.sqa_store.validation module

ax.storage.sqa_store.reduced_state module

ax.storage.sqa_store.reduced_state.get_query_options_to_defer_immutable_duplicates()List[sqlalchemy.orm.strategy_options.Load][source]

Returns the query options that defer loading of attributes that are duplicated on each trial (like search space attributes and metrics). These attributes do not need to be loaded for experiments with immutable search space and optimization configuration.

ax.storage.sqa_store.reduced_state.get_query_options_to_defer_large_model_cols()List[sqlalchemy.orm.strategy_options.Load][source]

Returns the query options that defer loading of model-state-related columns of generator runs, which can be large and are not needed on every generator run when loading experiment and generation strategy in reduced state.

Registries

ax.storage.botorch_modular_registry.ACQUISITION_FUNCTION_REGISTRY: Dict[Type[botorch.acquisition.acquisition.AcquisitionFunction], str] = {<class 'botorch.acquisition.analytic.ExpectedImprovement'>: 'ExpectedImprovement', <class 'botorch.acquisition.multi_objective.monte_carlo.qExpectedHypervolumeImprovement'>: 'qExpectedHypervolumeImprovement', <class 'botorch.acquisition.multi_objective.monte_carlo.qNoisyExpectedHypervolumeImprovement'>: 'qNoisyExpectedHypervolumeImprovement', <class 'botorch.acquisition.monte_carlo.qExpectedImprovement'>: 'qExpectedImprovement', <class 'botorch.acquisition.knowledge_gradient.qKnowledgeGradient'>: 'qKnowledgeGradient', <class 'botorch.acquisition.max_value_entropy_search.qMaxValueEntropy'>: 'qMaxValueEntropy', <class 'botorch.acquisition.knowledge_gradient.qMultiFidelityKnowledgeGradient'>: 'qMultiFidelityKnowledgeGradient', <class 'botorch.acquisition.max_value_entropy_search.qMultiFidelityMaxValueEntropy'>: 'qMultiFidelityMaxValueEntropy', <class 'botorch.acquisition.monte_carlo.qNoisyExpectedImprovement'>: 'qNoisyExpectedImprovement'}

Mapping of BoTorch MarginalLogLikelihood classes to class name strings.

ax.storage.botorch_modular_registry.ACQUISITION_REGISTRY: Dict[Type[ax.models.torch.botorch_modular.acquisition.Acquisition], str] = {<class 'ax.models.torch.botorch_modular.acquisition.Acquisition'>: 'Acquisition'}

Mapping of BoTorch Model classes to class name strings.

ax.storage.botorch_modular_registry.CLASS_TO_REGISTRY: Dict[Any, Dict[Type[Any], str]] = {<class 'ax.models.torch.botorch_modular.acquisition.Acquisition'>: {<class 'ax.models.torch.botorch_modular.acquisition.Acquisition'>: 'Acquisition'}, <class 'botorch.acquisition.acquisition.AcquisitionFunction'>: {<class 'botorch.acquisition.analytic.ExpectedImprovement'>: 'ExpectedImprovement', <class 'botorch.acquisition.multi_objective.monte_carlo.qExpectedHypervolumeImprovement'>: 'qExpectedHypervolumeImprovement', <class 'botorch.acquisition.multi_objective.monte_carlo.qNoisyExpectedHypervolumeImprovement'>: 'qNoisyExpectedHypervolumeImprovement', <class 'botorch.acquisition.monte_carlo.qExpectedImprovement'>: 'qExpectedImprovement', <class 'botorch.acquisition.knowledge_gradient.qKnowledgeGradient'>: 'qKnowledgeGradient', <class 'botorch.acquisition.max_value_entropy_search.qMaxValueEntropy'>: 'qMaxValueEntropy', <class 'botorch.acquisition.knowledge_gradient.qMultiFidelityKnowledgeGradient'>: 'qMultiFidelityKnowledgeGradient', <class 'botorch.acquisition.max_value_entropy_search.qMultiFidelityMaxValueEntropy'>: 'qMultiFidelityMaxValueEntropy', <class 'botorch.acquisition.monte_carlo.qNoisyExpectedImprovement'>: 'qNoisyExpectedImprovement'}, <class 'gpytorch.mlls.marginal_log_likelihood.MarginalLogLikelihood'>: {<class 'gpytorch.mlls.exact_marginal_log_likelihood.ExactMarginalLogLikelihood'>: 'ExactMarginalLogLikelihood', <class 'gpytorch.mlls.sum_marginal_log_likelihood.SumMarginalLogLikelihood'>: 'SumMarginalLogLikelihood'}, <class 'botorch.models.model.Model'>: {<class 'botorch.models.gp_regression.FixedNoiseGP'>: 'FixedNoiseGP', <class 'botorch.models.gp_regression_fidelity.FixedNoiseMultiFidelityGP'>: 'FixedNoiseMultiFidelityGP', <class 'botorch.models.multitask.FixedNoiseMultiTaskGP'>: 'FixedNoiseMultiTaskGP', <class 'botorch.models.gp_regression_mixed.MixedSingleTaskGP'>: 'MixedSingleTaskGP', <class 'botorch.models.model_list_gp_regression.ModelListGP'>: 'ModelListGP', <class 'botorch.models.multitask.MultiTaskGP'>: 'MultiTaskGP', <class 'botorch.models.gp_regression.SingleTaskGP'>: 'SingleTaskGP', <class 'botorch.models.gp_regression_fidelity.SingleTaskMultiFidelityGP'>: 'SingleTaskMultiFidelityGP'}}

Reverse registries for decoding.

ax.storage.botorch_modular_registry.MLL_REGISTRY: Dict[Type[gpytorch.mlls.marginal_log_likelihood.MarginalLogLikelihood], str] = {<class 'gpytorch.mlls.exact_marginal_log_likelihood.ExactMarginalLogLikelihood'>: 'ExactMarginalLogLikelihood', <class 'gpytorch.mlls.sum_marginal_log_likelihood.SumMarginalLogLikelihood'>: 'SumMarginalLogLikelihood'}

Overarching mapping from encoded classes to registry map.

ax.storage.botorch_modular_registry.MODEL_REGISTRY: Dict[Type[botorch.models.model.Model], str] = {<class 'botorch.models.gp_regression.FixedNoiseGP'>: 'FixedNoiseGP', <class 'botorch.models.gp_regression_fidelity.FixedNoiseMultiFidelityGP'>: 'FixedNoiseMultiFidelityGP', <class 'botorch.models.multitask.FixedNoiseMultiTaskGP'>: 'FixedNoiseMultiTaskGP', <class 'botorch.models.gp_regression_mixed.MixedSingleTaskGP'>: 'MixedSingleTaskGP', <class 'botorch.models.model_list_gp_regression.ModelListGP'>: 'ModelListGP', <class 'botorch.models.multitask.MultiTaskGP'>: 'MultiTaskGP', <class 'botorch.models.gp_regression.SingleTaskGP'>: 'SingleTaskGP', <class 'botorch.models.gp_regression_fidelity.SingleTaskMultiFidelityGP'>: 'SingleTaskMultiFidelityGP'}

Mapping of Botorch AcquisitionFunction classes to class name strings.

ax.storage.botorch_modular_registry.REVERSE_MLL_REGISTRY: Dict[str, Type[gpytorch.mlls.marginal_log_likelihood.MarginalLogLikelihood]] = {'ExactMarginalLogLikelihood': <class 'gpytorch.mlls.exact_marginal_log_likelihood.ExactMarginalLogLikelihood'>, 'SumMarginalLogLikelihood': <class 'gpytorch.mlls.sum_marginal_log_likelihood.SumMarginalLogLikelihood'>}

Overarching mapping from encoded classes to reverse registry map.

ax.storage.botorch_modular_registry.register_acquisition(acq_class: Type[ax.models.torch.botorch_modular.acquisition.Acquisition])None[source]

Add a custom acquisition class to the SQA and JSON registries.

ax.storage.metric_registry.register_metric(metric_cls: Type[ax.core.metric.Metric], val: Optional[int] = None)None[source]

Add a custom metric class to the SQA and JSON registries. For the SQA registry, if no int is specified, use a hash of the class name.

ax.storage.runner_registry.register_runner(runner_cls: Type[ax.core.runner.Runner], val: Optional[int] = None)None[source]

Add a custom runner class to the SQA and JSON registries. For the SQA registry, if no int is specified, use a hash of the class name.

Utilities

class ax.storage.utils.DomainType(value)[source]

Bases: enum.Enum

Class for enumerating domain types.

CHOICE: int = 2
FIXED: int = 0
RANGE: int = 1
class ax.storage.utils.MetricIntent(value)[source]

Bases: enum.Enum

Class for enumerating metric use types.

ADDITIONAL_OBJECTIVE: str = 'additional_objective'
MULTI_OBJECTIVE: str = 'multi_objective'
OBJECTIVE: str = 'objective'
OBJECTIVE_THRESHOLD: str = 'objective_threshold'
OUTCOME_CONSTRAINT: str = 'outcome_constraint'
SCALARIZED_OBJECTIVE: str = 'scalarized_objective'
SCALARIZED_OUTCOME_CONSTRAINT: str = 'scalarized_outcome_constraint'
TRACKING: str = 'tracking'
class ax.storage.utils.ParameterConstraintType(value)[source]

Bases: enum.Enum

Class for enumerating parameter constraint types.

Linear constraint is base type whereas other constraint types are special types of linear constraints.

LINEAR: int = 0
ORDER: int = 1
SUM: int = 2