blackboxopt.optimizers.testing

Tests that can be imported and used to test optimizer implementations against this packages blackbox optimizer interface.

handles_conditional_space(optimizer_class, optimizer_kwargs)

Check if optimizer handles conditional i.e. hierarchical search spaces.

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	Optimizer to test.	required
optimizer_kwargs	dict	Expected to contain additional arguments for initializing the optimizer. (search_space and objective(s) are set automatically by the test.)	required

Returns:

Type	Description
bool	True if the test is passed.

Source code in blackboxopt/optimizers/testing.py

def handles_conditional_space(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
) -> bool:
    """Check if optimizer handles conditional i.e. hierarchical search spaces.

    Args:
        optimizer_class: Optimizer to test.
        optimizer_kwargs: Expected to contain additional arguments for initializing
            the optimizer. (`search_space` and `objective(s)` are set automatically
            by the test.)

    Returns:
        `True` if the test is passed.
    """
    space = ps.ParameterSpace()
    space.add(ps.CategoricalParameter("optimizer", ("adam", "sgd")))
    space.add(ps.ContinuousParameter("lr", (0.0001, 0.1), transformation="log"))
    space.add(
        ps.ContinuousParameter("momentum", (0.0, 1.0)),
        lambda optimizer: optimizer == "sgd",
    )

    opt = _initialize_optimizer(
        optimizer_class,
        optimizer_kwargs,
        objective=Objective("loss", False),
        objectives=[Objective("loss", False)],
        space=space,
    )

    for _ in range(10):
        es = opt.generate_evaluation_specification()
        dummy_loss = es.configuration.get("momentum", 1.0) * es.configuration["lr"] ** 2
        opt.report(es.create_evaluation({"loss": dummy_loss}))

    return True

handles_reporting_evaluations_list(optimizer_class, optimizer_kwargs)

Check if optimizer's report method can process an iterable of evaluations.

All optimizers should be able to allow reporting batches of evaluations. It's up to the optimizer's implementation, if evaluations in a batch are processed one by one like if they were reported individually, or if a batch is handled differently.

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	Optimizer to test.	required
optimizer_kwargs	dict	Expected to contain additional arguments for initializing the optimizer. (search_space and objective(s) are set automatically by the test.)	required

Returns:

Type	Description
bool	True if the test is passed.

Source code in blackboxopt/optimizers/testing.py

def handles_reporting_evaluations_list(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
) -> bool:
    """Check if optimizer's report method can process an iterable of evaluations.

    All optimizers should be able to allow reporting batches of evaluations. It's up to
    the optimizer's implementation, if evaluations in a batch are processed
    one by one like if they were reported individually, or if a batch is handled
    differently.

    Args:
        optimizer_class: Optimizer to test.
        optimizer_kwargs: Expected to contain additional arguments for initializing
            the optimizer. (`search_space` and `objective(s)` are set automatically
            by the test.)

    Returns:
        `True` if the test is passed.
    """
    opt = _initialize_optimizer(
        optimizer_class,
        optimizer_kwargs,
        objective=Objective("loss", False),
        objectives=[Objective("loss", False)],
    )
    evaluations = []
    for _ in range(3):
        es = opt.generate_evaluation_specification()
        evaluation = es.create_evaluation(objectives={"loss": 0.42})
        evaluations.append(evaluation)

    opt.report(evaluations)
    return True

is_deterministic_with_fixed_seed(optimizer_class, optimizer_kwargs)

Check if optimizer is deterministic.

Repeatedly initialize the optimizer with the same parameter space and a fixed seed, get an evaluation specification, report a placeholder result and get another evaluation specification. The configuration of all final evaluation specifications should be equal.

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	Optimizer to test.	required
optimizer_kwargs	dict	Expected to contain additional arguments for initializing the optimizer. (search_space and objective(s) are set automatically by the test.)	required

Returns:

Type	Description
bool	True if the test is passed.

Source code in blackboxopt/optimizers/testing.py

def is_deterministic_with_fixed_seed(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
) -> bool:
    """Check if optimizer is deterministic.

    Repeatedly initialize the optimizer with the same parameter space and a fixed seed,
    get an evaluation specification, report a placeholder result and get another
    evaluation specification. The configuration of all final evaluation specifications
    should be equal.

    Args:
        optimizer_class: Optimizer to test.
        optimizer_kwargs: Expected to contain additional arguments for initializing
            the optimizer. (`search_space` and `objective(s)` are set automatically
            by the test.)

    Returns:
        `True` if the test is passed.
    """
    final_configurations = []

    for _ in range(2):
        opt = _initialize_optimizer(
            optimizer_class,
            optimizer_kwargs,
            objective=Objective("loss", False),
            objectives=[Objective("loss", False)],
        )

        es1 = opt.generate_evaluation_specification()
        evaluation1 = es1.create_evaluation(objectives={"loss": 0.42})
        opt.report(evaluation1)
        es2 = opt.generate_evaluation_specification()

        final_configurations.append(es2.configuration.copy())

    assert final_configurations[0] == final_configurations[1]
    return True

optimize_single_parameter_sequentially_for_n_max_evaluations(optimizer_class, optimizer_kwargs, n_max_evaluations=20)

[summary]

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	[description]	required
optimizer_kwargs	dict	[description]	required
n_max_evaluations	int	[description]	20

Returns:

Type	Description
bool	[description]

Source code in blackboxopt/optimizers/testing.py

def optimize_single_parameter_sequentially_for_n_max_evaluations(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
    n_max_evaluations: int = 20,
) -> bool:
    """[summary]

    Args:
        optimizer_class: [description]
        optimizer_kwargs: [description]
        n_max_evaluations: [description]

    Returns:
        [description]
    """

    def quadratic_function(p1):
        return p1**2

    assert issubclass(optimizer_class, Optimizer), (
        "The default test suite is only applicable for implementations of "
        "blackboxopt.base.Optimizer"
    )

    optimizer = _initialize_optimizer(
        optimizer_class,
        optimizer_kwargs,
        objective=Objective("loss", False),
        objectives=[Objective("loss", False), Objective("score", True)],
    )

    eval_spec = optimizer.generate_evaluation_specification()

    if issubclass(optimizer_class, MultiObjectiveOptimizer):
        evaluation = eval_spec.create_evaluation(
            objectives={"loss": None, "score": None}
        )
    else:
        evaluation = eval_spec.create_evaluation(objectives={"loss": None})
    optimizer.report(evaluation)

    for _ in range(n_max_evaluations):

        try:
            eval_spec = optimizer.generate_evaluation_specification()
        except OptimizationComplete:
            break

        loss = quadratic_function(p1=eval_spec.configuration["p1"])
        if issubclass(optimizer_class, MultiObjectiveOptimizer):
            evaluation_result = {"loss": loss, "score": -loss}
        else:
            evaluation_result = {"loss": loss}

        evaluation = eval_spec.create_evaluation(objectives=evaluation_result)
        optimizer.report(evaluation)

    return True

raises_evaluation_error_when_reporting_unknown_objective(optimizer_class, optimizer_kwargs)

Check if optimizer's report method raises exception in case objective is unknown.

Also make sure that the faulty evaluations (and only those) are included in the exception.

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	Optimizer to test.	required
optimizer_kwargs	dict	Expected to contain additional arguments for initializing the optimizer. (search_space and objective(s) are set automatically by the test.)	required

Returns:

Type	Description
bool	True if the test is passed.

Source code in blackboxopt/optimizers/testing.py

def raises_evaluation_error_when_reporting_unknown_objective(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
) -> bool:
    """Check if optimizer's report method raises exception in case objective is unknown.

    Also make sure that the faulty evaluations (and only those) are included in the
    exception.

    Args:
        optimizer_class: Optimizer to test.
        optimizer_kwargs: Expected to contain additional arguments for initializing
            the optimizer. (`search_space` and `objective(s)` are set automatically
            by the test.)

    Returns:
        `True` if the test is passed.
    """
    opt = _initialize_optimizer(
        optimizer_class,
        optimizer_kwargs,
        objective=Objective("loss", False),
        objectives=[Objective("loss", False)],
    )
    es_1 = opt.generate_evaluation_specification()
    es_2 = opt.generate_evaluation_specification()
    es_3 = opt.generate_evaluation_specification()

    # NOTE: The following is not using pytest.raises because this would add pytest as
    #       a regular dependency to blackboxopt.
    try:
        evaluation_1 = es_1.create_evaluation(objectives={"loss": 1})
        evaluation_2 = es_2.create_evaluation(objectives={"unknown_objective": 2})
        evaluation_3 = es_3.create_evaluation(objectives={"loss": 4})
        opt.report([evaluation_1, evaluation_2, evaluation_3])

        raise AssertionError(
            f"Optimizer {optimizer_class} did not raise an ObjectivesError when a "
            + "result including an unknown objective name was reported."
        )

    except EvaluationsError as exception:
        invalid_evaluations = [e for e, _ in exception.evaluations_with_errors]
        assert len(invalid_evaluations) == 1
        assert evaluation_2 in invalid_evaluations

    return True

respects_fixed_parameter(optimizer_class, optimizer_kwargs)

Check if optimizer's generated evaluation specifications contain the values a parameter in the search space was fixed to.

Parameters:

Name	Type	Description	Default
optimizer_class	Union[Type[blackboxopt.base.SingleObjectiveOptimizer], Type[blackboxopt.base.MultiObjectiveOptimizer]]	Optimizer to test.	required
optimizer_kwargs	dict	Expected to contain additional arguments for initializing the optimizer. (search_space and objective(s) are set automatically by the test.)	required

Returns:

Type	Description
bool	True if the test is passed.

Source code in blackboxopt/optimizers/testing.py

def respects_fixed_parameter(
    optimizer_class: Union[
        Type[SingleObjectiveOptimizer], Type[MultiObjectiveOptimizer]
    ],
    optimizer_kwargs: dict,
) -> bool:
    """Check if optimizer's generated evaluation specifications contain the values
    a parameter in the search space was fixed to.

    Args:
        optimizer_class: Optimizer to test.
        optimizer_kwargs: Expected to contain additional arguments for initializing
            the optimizer. (`search_space` and `objective(s)` are set automatically
            by the test.)

    Returns:
        `True` if the test is passed.
    """
    space = ps.ParameterSpace()
    space.add(ps.ContinuousParameter("my_fixed_param", (-10.0, 200.0)))
    space.add(ps.ContinuousParameter("x", (-2.0, 2.0)))

    fixed_value = 1.0
    space.fix(my_fixed_param=fixed_value)
    opt = _initialize_optimizer(
        optimizer_class,
        optimizer_kwargs,
        objective=Objective("loss", False),
        objectives=[Objective("loss", False)],
        space=space,
    )
    for _ in range(5):
        es = opt.generate_evaluation_specification()
        assert es.configuration["my_fixed_param"] == fixed_value
        opt.report(
            es.create_evaluation(objectives={"loss": es.configuration["x"] ** 2})
        )

    return True