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

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_batch, measures_batch[, ...])	Returns info for solutions from `ask()`.
`tell_dqd`(objective_batch, measures_batch, ...)	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_batch, measures_batch, metadata_batch=None)[source]¶

Returns info for solutions from ask().

Note

The objective batch, measures batch, and metadata batch must be in the same order as the solutions created by ask_dqd(); i.e. objective_batch[i], measures_batch[i], and metadata_batch[i] should be the objective, measures, and metadata for solution_batch[i].

Parameters

objective_batch ((batch_size,) array) – Each entry of this array contains the objective function evaluation of a solution.
measures_batch ((batch_size, measures_dm) array) – Each row of this array contains a solution’s coordinates in measure space.
metadata_batch ((batch_size,) array) – Each entry of this array contains an object holding metadata for a solution.

Raises

RuntimeError – This method is called without first calling ask().
ValueError – objective_batch, measures_batch, or metadata has the wrong shape.

tell_dqd(objective_batch, measures_batch, jacobian_batch, metadata_batch=None)[source]¶

Returns info for solutions from ask_dqd().

Note

The objective batch, measures batch, jacobian batch, and metadata batch must be in the same order as the solutions created by ask_dqd(); i.e. objective_batch[i], measures_batch[i], jacobian_batch[i], and metadata_batch[i] should be the objective, measures, jacobian, and metadata for solution_batch[i].

Parameters

objective_batch ((batch_size,) array) – Each entry of this array contains the objective function evaluation of a solution.
measures_batch ((batch_size, measure_dim) array) – Each row of this array contains a solution’s coordinates in measure space.
jacobian_batch (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.
metadata_batch ((batch_size,) array) – Each entry of this array contains an object holding metadata for a solution.

Raises

RuntimeError – This method is called without first calling ask().
ValueError – objective_batch, measures_batch, or metadata_batch has the wrong shape.

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