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Initial design

make_initial_design #

make_initial_design(
    *,
    parameters: Mapping[str, Parameter],
    encoder: ConfigEncoder,
    sampler: Literal["sobol", "prior", "uniform"] | Sampler,
    sample_size: int | Literal["ndim"] | None = "ndim",
    sample_prior_first: bool = True,
    seed: Generator | None = None
) -> list[dict[str, Any]]

Generate the initial design of the optimization process.

PARAMETER DESCRIPTION
space

The search space to use.
encoder

The encoder to use for encoding/decoding configurations.

TYPE: ConfigEncoder

sampler

The sampler to use for the initial design.

If set to "sobol", a Sobol sequence will be used. If set to "uniform", a uniform random sampler will be used. If set to "prior", a prior sampler will be used, based on the defaults, and confidence scores of the hyperparameters. If set to a custom sampler, the sampler will be used directly.

TYPE: Literal['sobol', 'prior', 'uniform'] | Sampler

sample_size

The number of configurations to sample.

If "ndim", the number of configs will be equal to the number of dimensions. If None, no configurations will be sampled.

TYPE: int | Literal['ndim'] | None DEFAULT: 'ndim'

sample_prior_first

Whether to sample the prior configuration first.

TYPE: bool DEFAULT: True

seed

The seed to use for the random number generation.

TYPE: Generator | None DEFAULT: None

Source code in neps\optimizers\utils\initial_design.py 
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def make_initial_design(
    *,
    parameters: Mapping[str, Parameter],
    encoder: ConfigEncoder,
    sampler: Literal["sobol", "prior", "uniform"] | Sampler,
    sample_size: int | Literal["ndim"] | None = "ndim",
    sample_prior_first: bool = True,
    seed: torch.Generator | None = None,
) -> list[dict[str, Any]]:
    """Generate the initial design of the optimization process.

    Args:
        space: The search space to use.
        encoder: The encoder to use for encoding/decoding configurations.
        sampler: The sampler to use for the initial design.

            If set to "sobol", a Sobol sequence will be used.
            If set to "uniform", a uniform random sampler will be used.
            If set to "prior", a prior sampler will be used, based on the defaults,
                and confidence scores of the hyperparameters.
            If set to a custom sampler, the sampler will be used directly.

        sample_size:
            The number of configurations to sample.

            If "ndim", the number of configs will be equal to the number of dimensions.
            If None, no configurations will be sampled.

        sample_prior_first: Whether to sample the prior configuration first.
        seed: The seed to use for the random number generation.

    """
    configs: list[dict[str, Any]] = []
    if sample_prior_first:
        configs.append(
            {
                name: p.prior if p.prior is not None else p.center
                for name, p in parameters.items()
            }
        )

    ndims = len(parameters)
    if sample_size == "ndim":
        sample_size = ndims
    elif sample_size is not None and not sample_size > 0:
        raise ValueError(
            "The sample size should be a positive integer if passing an int."
        )

    if sample_size is not None:
        match sampler:
            case "sobol":
                sampler = Sampler.sobol(ndim=ndims)
            case "uniform":
                sampler = Sampler.uniform(ndim=ndims)
            case "prior":
                sampler = Prior.from_parameters(parameters)
            case _:
                pass

        encoded_configs = sampler.sample(sample_size * 2, to=encoder.domains, seed=seed)
        uniq_x = torch.unique(encoded_configs, dim=0)
        sample_configs = encoder.decode(uniq_x[:sample_size])
        configs.extend(sample_configs)

    return configs