Objectives

Objective

class blop.ax.objective.Objective(*, name, minimize)

Bases: object

An objective to optimize.

An objective represents a measurable outcome that you want to optimize. The optimizer will try to minimize or maximize this outcome based on the acquired data and evaluation function.

Attributes:

namestr

The name of the objective. This must match the key returned by the evaluation function for this outcome.

minimizebool

Whether to minimize or maximize the objective. Set to True for minimization (e.g., reducing beam width) or False for maximization (e.g., increasing intensity).

Examples

Define an objective to maximize beam intensity:

>>> from blop.ax.objective import Objective
>>> objective = Objective(name="beam_intensity", minimize=False)

Define an objective to minimize beam width:

>>> objective = Objective(name="beam_width", minimize=True)

name: str

minimize: bool

Scalarized Objective

class blop.ax.objective.ScalarizedObjective(expression, *, minimize, **objective_names)

Bases: object

A scalarized objective is a weighted sum of other objectives.

Use this to combine multiple objectives into a single optimization target when you want to optimize a weighted combination of outcomes. This is useful for multi-objective optimization where you have clear trade-off preferences.

Parameters:

expressionstr

The expression to evaluate containing the objectives.

minimizebool

Whether to minimize or maximize the expression.

**objective_namesstr

Keyword arguments mapping variables in the expression to objective names.

Notes

The variable names used in the expression (e.g., “intensity”, “width”) are arbitrary and do not need to match the objective names. They are mapped to objectives via the keyword arguments for readability.

Examples

Create a scalarized objective for minimizing a weighted sum:

>>> from blop.ax.objective import ScalarizedObjective
>>> scalarized_obj = ScalarizedObjective(
...     expression="x + 2 * y",
...     minimize=True,
...     x="objective1",
...     y="objective2",
... )
>>> print(scalarized_obj)
-(objective1 + 2 * objective2)

property ax_expression: str

Convert the scalarized objective to a string that can be used by Ax.

Returns:

str

The expression with objective names substituted, negated if minimizing.