Multi-Objective Optimization with Ax
Multi-objective optimization (MOO) allows you to optimize multiple objectives simultaneously, which is particularly useful when you have competing objectives. In this recipe, we will demonstrate how to perform multi-objective optimization using the Ax Client.
Note that while MOO can handle multiple objectives, it's generally recommended to keep the number of objectives relatively small. Having too many objectives can lead decreased optimization performance and difficulties in interpreting the results.
Prerequisites
We will assume you are already familiar with basic Ax usage.
Setup
Instantiate the Client and configure it with your experiment and metrics.
client = Client()
client.configure_experiment(...)
client.configure_metrics(...)
Steps
- Configure an optimization with multiple objectives
- Continue with iterating over trials and evaluating them
- Observe optimal parametrizations
1. Configure an optimization with multiple objectives
We can leverage the Client's configure_optimization method. This method takes
in an objective goal as a string, and can be used to specify single-objective,
scalarized-objective, and multi-objective goals. For this recipe, we will define
a multi-objective goal:
client.configure_optimization(objectives="-cost, utility")
By default, objectives are assumed to be maximized. If you want to minimize an
objective, you can prepend the objective with a - sign.
2. Continue with iterating over trials and evaluating them
Now that your experiment has been configured for a multi-objective optimization, you can simply continue with iterating over trials and evaluating them as you typically would.
# Getting just one trial in this example
trial_idx, parameters = client.get_next_trials(max_trials=1)().popitem()
client.complete_trial(...)
3. Observe optimal parametrizations
You can now observe the optimal parametrizations by calling
get_optimal_pareto_frontier. The function returns a list of tuples containing
the best parameters, their corresponding metric values, the most recent trial
that ran them, and the name of the best arm.
frontier = client.get_pareto_frontier()
for parameters, metrics, trial_index, arm_name in frontier:
...
Learn more
Take a look at these other resources to continue your learning: