Optimizers

def __init__(self, index_eval: IndexEvaluator, index: Index, **kwargs: Any) -> None:
    """
    Parameters:
        index_eval: The id evaluator. Evalutes the items at the given storage indices.
        index: The index that contains information about the domain.
        **kwargs: The keyword arguments.
    """

    # Included s.t. constructors of Index can be used.
    ...

def __init__(
    self,
    index_eval: IndexEvaluator,
    index: Index,
    *,
    sobol_steps: int = 0,
    device: Device = "cpu",
    workers: int = 1,
    task: Task = Task.EXPLORE,
    acqf: str | AcquisitionFunc = AcquisitionFunc.AUTO,
    surrogate: str | SurrogateModel = SurrogateModel.AUTO,
    surrogate_kwargs: SurrogateOptions | None = None,
) -> None:
    """
    Parameters:
        index_eval: The evaluator to use for the query.
        index: The index to for querying.
        sobol_steps: The number of steps to use for the Sobol sequence.
        device: The device to use for the optimization.
        workers: The number of workers to use for the optimization.
        task: The task to use for the optimization.
        acqf: The acquisition function to use for the optimization.
        surrogate: The surrogate model to use for the optimization.
        surrogate_kwargs: The keyword arguments to pass to the surrogate model.
    """

    silence_ax()

    acqf = AcquisitionFunc.lookup(acqf)
    task = Task.lookup(task)

    utils.check_acquisition_task_combo(acqf=acqf, task=task)

    self._device = device
    self._acqf = acqf
    self._surrogate = SurrogateModel.lookup(surrogate).surrogate(surrogate_kwargs)
    self._task = task

    self._ax_client = AxClient(generation_strategy=self._gen_strat(sobol_steps))
    self._create_experiment(index.boundary)

    self._index_eval = index_eval
    self._index = index
    self._workers = workers
    self._terminate = False

def __init__(
    self,
    index_eval: IndexEvaluator,
    index: Index,
    *,
    batch_size: int,
    embeddings: NDArray,
    model_kwargs: KMeansOptions,
) -> None:
    """
    Parameters:
        index_eval: The evaluator to use on the storage.
        index: The index to use for the query.
        batch_size: The number of embeddings to evaluate at once.
        embeddings: The embeddings to cluster.
        model_kwargs: The keyword arguments to pass to the KMeans model.
    """

    model = KMeans(**model_kwargs)

    super().__init__(
        index_eval=index_eval,
        index=index,
        embeddings=embeddings,
        model=model,
        batch_size=batch_size,
    )

    self._model_kwargs = model_kwargs

def __init__(
    self,
    index_eval: IndexEvaluator,
    index: Index,
    *,
    batch_size: int,
    embeddings: NDArray,
    model_kwargs: KMedoidsOptions,
) -> None:
    """
    Parameters:
        index_eval: The evaluator to use for the index.
        index: The index to use for the query.
        batch_size: The number of embeddings to evaluate at once.
        embeddings: The embeddings to cluster.
        model_kwargs: The keyword arguments to pass to the KMedoids model.
    """

    # Optional dependency.
    from sklearn_extra.cluster import KMedoids

    model = KMedoids(**model_kwargs)

    super().__init__(
        index_eval=index_eval,
        index=index,
        embeddings=embeddings,
        model=model,
        batch_size=batch_size,
    )

    self._model_kwargs = model_kwargs

def __init__(
    self,
    index_eval: IndexEvaluator,
    index: Index,
    *,
    samples: int,
    batch_size: int,
) -> None:
    """
    Parameters:
        index_eval: The evaluator to use for the storage.
        index: The index to use for the query.
        samples: The number of samples to use for the optimization.
        batch_size: The number of samples to evaluate at once.
    """

    LOGGER.info("Instantiating RandomOptimizer", total=len(index), samples=samples)

    self._index_eval = index_eval
    self._index = index
    self._generator = iter(BatchedGenerator(self._gen_random(samples), batch_size))

def __init__(
    self,
    index_eval: IndexEvaluator,
    index: Index,
    *,
    grids: Sequence[int],
    batch_size: int,
) -> None:
    """
    Parameters:
        index_eval: The evaluator to use for the storage.
        index: The index to use for the query.
        grids: The number of grids to use for the optimization.
        batch_size: The number of grids to evaluate at once.
    """

    LOGGER.info("Instantiating UnfiromOptimizer", grids=grids)

    self._index_eval = index_eval
    self._index = index

    self._generator = iter(BatchedGenerator(self._gen_locs(grids), batch_size))

    if len(grids) != self._index.dims:
        raise ValueError(f"Expected {self._index.dims} strides, got {grids}")