deephyper.skopt.utils.Grid#

class deephyper.skopt.utils.Grid(border='exclude', use_full_layout=True, append_border='only')[source]#

Bases: InitialPointGenerator

Generate samples from a regular grid.

Parameters:
  • border (str, default='exclude') – defines how the samples are generated: - ‘include’ : Includes the border into the grid layout - ‘exclude’ : Excludes the border from the grid layout - ‘only’ : Selects only points at the border of the dimension

  • use_full_layout (boolean, default=True) – When True, a full factorial design is generated and missing points are taken from the next larger full factorial design, depending on append_border When False, the next larger full factorial design is generated and points are randomly selected from it.

  • append_border (str, default="only") – When use_full_layout is True, this parameter defines how the missing points will be generated from the next larger grid layout: - ‘include’ : Includes the border into the grid layout - ‘exclude’ : Excludes the border from the grid layout - ‘only’ : Selects only points at the border of the dimension

Methods

generate

Creates samples from a regular grid.

set_params

Set the parameters of this initial point generator.

generate(dimensions, n_samples, random_state=None)[source]#

Creates samples from a regular grid.

Parameters:
  • dimensions (list, shape (n_dims,)) –

    List of search space dimensions. Each search dimension can be defined either as

    • a (lower_bound, upper_bound) tuple (for Real or Integer dimensions),

    • a (lower_bound, upper_bound, “prior”) tuple (for Real dimensions),

    • as a list of categories (for Categorical dimensions), or

    • an instance of a Dimension object (Real, Integer or Categorical).

  • n_samples (int) – The order of the Halton sequence. Defines the number of samples.

  • random_state (int, RandomState instance, or None (default)) – Set random state to something other than None for reproducible results.

Returns:

grid set

Return type:

np.array, shape=(n_dim, n_samples)

set_params(**params)#

Set the parameters of this initial point generator.

Parameters:

**params (dict) – Generator parameters.

Returns:

self – Generator instance.

Return type:

object