mbo_OptPath: OptPath in mlrMBO

In mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

OptPath in mlrMBO

Description

In mlrMBO the OptPath contains extra information next to the information documented in OptPath.

The extras are:

train.time

Time to train the model(s) that produced the points. Only the first slot of the vector is used (if we have multiple points), rest are NA.

propose.time

Time needed to propose the point. If we have individual timings from the proposal mechanism, we have one different value per point here. If all were generated in one go, we only have one timing, we store it in the slot for the first point, rest are NA.

errors.model

Possible Error Messages. If point-producing model(s) crashed they are replicated for all n points, if only one error message was passed we store it for the first point, rest are NA.

prop.type

Type of point proposal. Possible values are

initdesign

Points actually not proposed, but in the initial design.

infill_x

Here x is a placeholder for the selected infill criterion, e.g., infill_ei for expected improvement.

random_interleave

Uniformly sampled points added additionally to the proposed points.

random_filtered

If filtering of proposed points located too close to each other is active, these are replaced by random points.

final_eval

If final.evals is set in makeMBOControl: Final evaluations of the proposed solution to reduce noise in y.

parego.weight

Weight vector sampled for multi-point ParEGO

...

Depending on the chosen infill criterion there will be additional columns, e.g. se and mean for the Expected Improvement)

Moreover, the user may pass additional “user extras” by appending a named list of scalar values to the return value of the objective function.

mlrMBO documentation built on July 4, 2022, 9:05 a.m.