ribs.emitters.opt.PyCMAEvolutionStrategy

class ribs.emitters.opt.PyCMAEvolutionStrategy(sigma0, solution_dim, batch_size=None, seed=None, dtype=<class 'numpy.float64'>, lower_bounds=None, upper_bounds=None, opts=None)[source]

Wrapper around the pycma CMAEvolutionStrategy.

Parameters

  • sigma0 (float) – Initial step size.

  • batch_size (int or str) – Number of solutions to evaluate at a time. This is passed directly as popsize in opts.

  • solution_dim (int) – Size of the solution space.

  • seed (int) – Seed for the random number generator.

  • dtype (str or data-type) – Data type of solutions.

  • lower_bounds (float or np.ndarray) – scalar or (solution_dim,) array indicating lower bounds of the solution space. Scalars specify the same bound for the entire space, while arrays specify a bound for each dimension. Pass -np.inf in the array or scalar to indicated unbounded space.

  • upper_bounds (float or np.ndarray) – Same as above, but for upper bounds (and pass np.inf instead of -np.inf).

  • opts (dict) – Additional options for pycma. Note that popsize, bounds, randn, and seed are overwritten by us and thus should not be provided in this dict. We also make verbose default to -9, but you can also pass in a custom value here.

Methods

ask([batch_size])

Samples new solutions from the Gaussian distribution.

check_stop(ranking_values)

Checks if the optimization should stop and be reset.

reset(x0)

Resets the optimizer to start at x0.

tell(ranking_indices, ranking_values, ...)

Passes the solutions back to the ES.

Attributes

batch_size

Number of solutions per iteration.

ask(batch_size=None)[source]

Samples new solutions from the Gaussian distribution.

Parameters

batch_size (int) – batch size of the sample. Defaults to self.batch_size.

check_stop(ranking_values)[source]

Checks if the optimization should stop and be reset.

Parameters

ranking_values (np.ndarray) – Not used.

Returns

Output of cma.CMAEvolutionStrategy.stop

reset(x0)[source]

Resets the optimizer to start at x0.

Parameters

x0 (np.ndarray) – Initial mean.

tell(ranking_indices, ranking_values, num_parents)[source]

Passes the solutions back to the ES.

Parameters

  • ranking_indices (numpy.ndarray) – Integer indices that are used to rank the solutions returned in ask(). Note that these are NOT the ranks of the solutions. Rather, they are indices such that solutions[ranking_indices] will correctly rank the solutions (think of an argsort).

  • ranking_values (numpy.ndarray) – Array of values that were used to rank the solutions. Shape can be either (batch_size,) or (batch_size, n_values)``, where batch_size is the number of solutions and n_values is the number of values that the ranker used. Note that we assume a descending sort, i.e., higher values should come first.

  • num_parents (int) – Number of top solutions to select from the ranked solutions.

property batch_size

Number of solutions per iteration.

Only valid after a call to reset().

Type

int