Generators

Abstract Base Generator

class BaseGenerator[source]

Base generator from which all generators must inherit to be compatible with GloMPO.

Attributes:

loggerlogging.Logger

logging.Logger instance into which status messages may be added.

abstract generate(manager)[source]

Returns a vector representing a location in input space. The returned array serves as a starting point for an optimizer.

Parameters:

manager

GloMPOManager instance which is managing the optimization. Its attributes can be accessed when determining the new point.

Simple Generators

For convenience, GloMPO comes bundled with several simple generators already included.

class ExploitExploreGenerator(max_func_calls, focus=1)[source]

Bases: BaseGenerator

This generator blends a randomly generated point with the location of an existing optimizer. The optimizer is chosen based on a roulette selection.

Parameters:

max_func_calls

Maximum function calls allowed for the optimization, at and beyond this point there is a 100% chance that a previously evaluated point will be returned by the generator. If the optimization is not limited by the number of function calls, provide an estimate.

focus

The blend parameter between random point and incumbent points.

Notes:

focus is used as follows:

p=(f_calls / max_f_calls) ** focus

At p=0 the random point is taken. At p=1 the incumbent is chosen. If focus < 1 points are more like the incumbent, if focus > 1 points are more like the random. Default is focus = 1 which has a linear growth from random to incumbent. The new point is calculated as:

new_pt = p*incumbent_pt + (1-p)*random_pt.
class IncumbentGenerator[source]

Bases: BaseGenerator

Starts a new optimizer at GloMPOManager.result['x']. A random vector is generated if this is indeterminate.

class PerturbationGenerator(x0, scale)[source]

Bases: BaseGenerator

Randomly generates parameter vectors near a given point. Draws samples from a truncated multivariate normal distributed centered around a provided vector and bound by given bounds. Good for parametrisation efforts where a good candidate is already available, however, this may drastically limit the exploratory nature of GloMPO.

Parameters:

x0

Center point for each parameter

scale

Standard deviation of each parameter. Used here to control how wide the generator should explore around the mean.

class RandomGenerator[source]

Bases: BaseGenerator

Generates random points. Points are drawn from a uniform distribution within given bounds.

class SinglePointGenerator(x=None)[source]

Bases: BaseGenerator

Always returns the same point. Either provided during initialisation or otherwise randomly generated.