plot_slice with choiceParameter

[adnana-dev]

Hello everyone.

Is there any way to use plot_slice like thisrender(plot_slice(ax_client.generation_strategy.model, "x", "dsp")) despite using choiceParameter?

gs = GenerationStrategy(
    steps=[

        # 2. Bayesian optimization step (requires data obtained from previous phase and learns
        # from all data available at the time of each new candidate generation call)
        GenerationStep(
            model=Models.GPEI,
            num_trials=-1,  # No limitation on how many trials should be produced from this step
            max_parallelism=1,  # Parallelism limit for this step, often lower than for Sobol
            # More on parallelism vs. required samples in BayesOpt:
            # https://ax.dev/docs/bayesopt.html#tradeoff-between-parallelism-and-total-number-of-trials
        ),
    ]
)

ax_client = AxClient(generation_strategy=gs)

ax_client.create_experiment(
    name="test",
    parameters=[
        {
            "name": "x",
            "type": "choice",
            "value_type": "float",
            "values": list(np.arange(8.0, 15.0, 0.25)),  # Convertissez les éléments en type float natif.
            "is_ordered": True,  # Indique que les valeurs sont ordonnées.
        },
    ],
    choose_generation_strategy_kwargs={"max_parallelism_override": 1},
    objective_name="dsp",
    minimize=True,  # Optional, defaults to False.
    outcome_constraints=["alpha >= 0",
                         "beta >= 0",
                         "gamma >= 0",
                         "delta >= 0",
                         "epsilon >= 0",
                         "iota >= 0",
                         "kappa >= 0",
                        ],  # Optional.
)

[mpolson64]

Hi Adama, sorry to have taken a while to get back to you on this. Conceptually I believe this is something we should support but it will take some engineering work on our end to make it happen. Marking this as wishlist for now, and hopefully our team can prioritize it soon. I will update this Issue once the feature has made it into a release.