Targets¶

Targets play a crucial role as the connection between observables measured in an experiment and the machine learning core behind BayBE. In general, it is expected that you create one Target object for each of your observables. The way BayBE treats multiple targets is then controlled via the Objective.

NumericalTarget¶

Besides the name, a NumericalTarget has the following attributes:

The optimization mode: Specifies whether we want to minimize/maximize the target or whether we want to match a specific value.
Bounds: Defines bounds that constrain the range of target values.
A transformation function: When bounds are provided, this is used to map target values into the [0, 1] interval.

Below is a visualization of possible choices for transformation, where lower and upper are the entries provided via bounds: Transforms

MIN and MAX mode¶

Here are two examples for simple maximization and minimization targets:

from baybe.targets import NumericalTarget, TargetMode, TargetTransformation

max_target = NumericalTarget(
    name="Target_1",
    mode=TargetMode.MAX,  # can also be provided as string "MAX"
)

min_target = NumericalTarget(
    name="Target_2",
    mode="MIN",  # can also be provided as TargetMode.MIN
    bounds=(0, 100),  # optional
    transformation=TargetTransformation.LINEAR,  # optional, will be applied if bounds are not None
)

MATCH mode¶

If you want to match a desired value, the TargetMode.MATCH mode is the right choice. In this mode, bounds are required and different transformations compared to MIN and MAX modes are allowed.

Assume we want to instruct BayBE to match a value of 50 in a target. We simply need to choose the bounds so that the midpoint is the desired value. The spread of the bounds interval defines how fast the acceptability of a measurement falls off away from the match value, also depending on the choice of transformation.

In the example below, match_targetA will treat all values < 45 and > 55 as equally bad, while match_targetB is more forgiving in that it chooses a bell curve transformation instead of a triangular one, and also uses a wider interval of bounds. Both targets are configured such that the midpoint of bounds (in this case 50) becomes the optimal value:

from baybe.targets import NumericalTarget, TargetMode, TargetTransformation

match_targetA = NumericalTarget(
    name="Target_3A",
    mode=TargetMode.MATCH,
    bounds=(45, 55),  # mandatory in MATCH mode
    transformation=TargetTransformation.TRIANGULAR,  # optional, applied if bounds are not None
)
match_targetB = NumericalTarget(
    name="Target_3B",
    mode="MATCH",
    bounds=(0, 100),  # mandatory in MATCH mode
    transformation="BELL",  # can also be provided as TargetTransformation.BELL
)

Targets are used in nearly all examples.

Limitations¶

Important

At the moment, BayBE’s only option for targets is the NumericalTarget. This enables many use cases due to the real-valued nature of most measurements. But it can also be used to model categorial targets if they are ordinal. For example: If your experimental outcome is a categorical ranking into “bad”, “mediocre” and “good”, you could use a NumericalTarget with bounds (1, 3), where the categories correspond to values 1, 2 and 3 respectively. If your target category is not ordinal, the transformation into a numerical target is not straightforward, which is a current limitation of BayBE. We are looking into adding more target options in the future.