Open Rykath opened 2 years ago
While looking into this issue I noticed that the Active Learning ignores the distinction between regular Input variables and ActiveLearning variables during the learn
step.
Workaround (in progress): specifying searchtype: halton
fixes the number of points to nsearch
active_learning:
nwarmup: 5
nsearch: 1000
algorithm:
class: simple
searchtype: halton
acquisition_function:
class: simple_exploration
in comparison the default searchtype: grid
uses nsearch: 50
points per dimension
active_learning:
nwarmup: 5
nsearch: 50
algorithm:
class: simple
searchtype: grid
acquisition_function:
class: simple_exploration
Configuring a moderately large number (5-10) of input variables for Active Learning will fail as the search space no longer fits into memory. Required by: MEPHIT (#174)
SimpleAL
uses a meshgrid over all AL-inputs as search space. The required space scales withnsearch^ninputs
.Workaround:
Possible Solutions
Acquisition functions use a loss or utility function and select the maximum/minimum based on the surrogate predictions for all points within the search space.
SimpleAL
Xpred
has to be modifiedAt this point the question also arises whether the structure of Active Learning / acquisition functions should be refactored to simplify the API? Which changes are necessary to solve this issue?