For Multi-objective optimization (MOO) in the AxClient, objectives are specified through the ObjectiveProperties dataclass. An ObjectiveProperties requires a boolean minimize, and also accepts an optional floating point threshold. If a threshold is not specified, Ax will infer it through the use of heuristics. If the user knows the region of interest (because they have specs or prior knowledge), then specifying the thresholds is preferable to inferring it. But if the user would need to guess, inferring is preferable.
To learn more about how to choose a threshold, see Set Objective Thresholds to focus candidate generation in a region of interest. See the Service API Tutorial for more infomation on running experiments with the Service API.
from ax.service.ax_client import AxClient
from ax.service.utils.instantiation import ObjectiveProperties
import torch
# Plotting imports and initialization
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.plot.pareto_utils import compute_posterior_pareto_frontier
from ax.plot.pareto_frontier import plot_pareto_frontier
init_notebook_plotting()
# Load our sample 2-objective problem
from botorch.test_functions.multi_objective import BraninCurrin
branin_currin = BraninCurrin(negate=True).to(
dtype=torch.double,
device= torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)
[INFO 12-29 21:56:58] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.