ribs.schedulers.Scheduler

class ribs.schedulers.Scheduler(archive, emitters, *, result_archive=None, add_mode='batch')[source]

A basic class that composes an archive with multiple emitters.

To use this class, first create an archive and list of emitters for the QD algorithm. Then, construct the Scheduler with these arguments. Finally, repeatedly call ask() to collect solutions to analyze, and return the objective and measures of those solutions in the same order using tell().

As all solutions go into the same archive, the emitters passed in must emit solutions with the same dimension (that is, their solution_dim attribute must be the same).

Warning

If you are constructing many emitters at once, do not do something like [EmitterClass(...)] * 5, as this creates a list with the same instance of EmitterClass in each position. Instead, use [EmitterClass(...) for _ in range 5], which creates 5 unique instances of EmitterClass.

Parameters
  • archive (ribs.archives.ArchiveBase) – An archive object, e.g. one selected from ribs.archives.

  • emitters (list of ribs.archives.EmitterBase) – A list of emitter objects, e.g. ribs.emitters.GaussianEmitter.

  • add_mode (str) – Indicates how solutions should be added to the archive. The default is “batch”, which adds all solutions with one call to add(). Alternatively, use “single” to add the solutions one at a time with add_single(). “single” mode is included for legacy reasons, as it was the only mode of operation in pyribs 0.4.0 and before. We highly recommend using “batch” mode since it is significantly faster.

  • result_archive (ribs.archives.ArchiveBase) – In some algorithms, such as CMA-MAE, the archive does not store all the best-performing solutions. The result_archive is a secondary archive where we can store all the best-performing solutions.

Raises
  • TypeError – The emitters argument was not a list of emitters.

  • ValueError – The emitters passed in do not have the same solution dimensions.

  • ValueError – There is no emitter passed in.

  • ValueError – The same emitter instance was passed in multiple times. Each emitter should be a unique instance (see the warning above).

  • ValueError – Invalid value for add_mode.

Methods

ask()

Generates a batch of solutions by calling ask() on all emitters.

ask_dqd()

Generates a batch of solutions by calling ask_dqd() on all DQD emitters.

tell(objective, measures, **fields)

Returns info for solutions from ask().

tell_dqd(objective, measures, jacobian, **fields)

Returns info for solutions from ask_dqd().

Attributes

EMPTY_WARNING

archive

Archive for storing solutions found in this scheduler.

emitters

Emitters for generating solutions in this scheduler.

result_archive

Another archive for storing solutions found in this optimizer.

ask()[source]

Generates a batch of solutions by calling ask() on all emitters.

Note

The order of the solutions returned from this method is important, so do not rearrange them.

Returns

An array of n solutions to evaluate. Each row contains a single solution.

Return type

(batch_size, dim) array

Raises

RuntimeError – This method was called without first calling tell().

ask_dqd()[source]

Generates a batch of solutions by calling ask_dqd() on all DQD emitters.

Note

The order of the solutions returned from this method is important, so do not rearrange them.

Returns

An array of n solutions to evaluate. Each row contains a single solution.

Return type

(batch_size, dim) array

Raises

RuntimeError – This method was called without first calling tell().

tell(objective, measures, **fields)[source]

Returns info for solutions from ask().

Note

The objective and measures arrays must be in the same order as the solutions created by ask_dqd(); i.e. objective[i] and measures[i] should be the objective and measures for solution[i].

Parameters
  • objective ((batch_size,) array) – Each entry of this array contains the objective function evaluation of a solution.

  • measures ((batch_size, measures_dm) array) – Each row of this array contains a solution’s coordinates in measure space.

  • fields (keyword arguments) – Additional data for each solution. Each argument should be an array with batch_size as the first dimension.

Raises
tell_dqd(objective, measures, jacobian, **fields)[source]

Returns info for solutions from ask_dqd().

Note

The objective, measures, and jacobian arrays must be in the same order as the solutions created by ask_dqd(); i.e. objective[i], measures[i], and jacobian[i] should be the objective, measures, and jacobian for solution[i].

Parameters
  • objective ((batch_size,) array) – Each entry of this array contains the objective function evaluation of a solution.

  • measures ((batch_size, measure_dim) array) – Each row of this array contains a solution’s coordinates in measure space.

  • jacobian (numpy.ndarray) – (batch_size, 1 + measure_dim, solution_dim) array consisting of Jacobian matrices of the solutions obtained from ask_dqd(). Each matrix should consist of the objective gradient of the solution followed by the measure gradients.

  • fields (keyword arguments) – Additional data for each solution. Each argument should be an array with batch_size as the first dimension.

Raises
property archive

Archive for storing solutions found in this scheduler.

Type

ribs.archives.ArchiveBase

property emitters

Emitters for generating solutions in this scheduler.

Type

list of ribs.archives.EmitterBase

property result_archive

Another archive for storing solutions found in this optimizer. If result_archive was not passed to the constructor, this property is the same as archive.

Type

ribs.archives.ArchiveBase