In updating Optimas generators for the standard, how do we deal with these extra fields in VaryingParameter.

    lower_bound: float
    upper_bound: float
    is_fidelity: Optional[bool] = False
    fidelity_target_value: Optional[float] = None
    default_value: Optional[float] = None
    _is_fixed: bool = PrivateAttr(False)

re: https://github.com/optimas-org/optimas/blob/main/optimas/core/parameter.py#L79

In the standard, variables have default_value & domain, but no equivalent to the others.

https://github.com/campa-consortium/generator_standard/blob/main/generator_standard/vocs.py#L16

Do we need is_fidelity / fidelity_target_value for each parameter? Or could it be a generator option?
(fidelity_target_value is given to Ax as the fidelity we want to optimize for).

is_fixed allows fixing a parameter value between calls to run(). Generator still has to support it.

e.g.

    var1 = VaryingParameter("x0", -50.0, 5.0)
    var2 = VaryingParameter("x1", -5.0, 15.0)

    # Start with a fixed value of x0.
    var1.fix_value(-10.0)
    ...
    exploration.run(n_evals=5)
    var1.free_value()
    gen.update_parameter(var1)
    exploration.run(n_evals=5)

could be instructed directly to generator instead - for supporting gens.

e.g.

gen.fix_var('x0', -10.0)

Alternatively, we could add fields to the standard, or create subclasses to ContinuousVariable for Optimas. This may be more elegant.

Currently, I have used the following generator functions (shown with example calls) to set the Optimas internals for these, without adding to or subclassing ContinuousVariable in VOCS.

gen.fix_value("x0", -10.0)
gen.update_range("x0", -20.0, 0.0)
gen.set_fidelity_param("res", is_fidelity=True, fidelity_target_value=8.0)

These are used in test_ax_generators.py.

Observables (analysed parameters) in Optimas can specify a type, but this isn’t supported in the standard interface. A type is necessary for constructing a NumPy structured array (for libEnsemble).

For example, in this test, the type must be a string.

https://github.com/optimas-org/optimas/blob/main/tests/test_env_script.py#L27

In test_template_evaluator.py and test_function_evaluator.py it stores an array and a plot object.

I have suggested we allow specifying a type for observables and constants in the standard.
campa-consortium/gest-api#39

The standard does not support arrays generally, but I have made it possible to specify a string or a tuple for observable type, which means numpy arrays can be used.
campa-consortium/gest-api#40

e.g.

def test_template_evaluator():
    # Define variables and objectives.
    vocs = VOCS(
        variables={"x0": [-50.0, 5.0], "x1": [-5.0, 15.0]},
        objectives={"f": "MAXIMIZE"},
        observables={
            "p0": (float, (2, 4)),
            "p1": "O",
            "fig": "O",
        },
    )

    # Define variables and objectives.
    gen = RandomSamplingGenerator(vocs=vocs)

exploration_diagnostics:

exploration_diagnostics.py:
_create_exploration Still reads from a file which will contain data in the old Optimus format - this should be updated.

test_exploration_diagnostics.py:
Removes a check that now seems redundant

        # Check that all possible objective inputs give the same result.
        _, trace1 = diags.get_objective_trace()
        _, trace2 = diags.get_objective_trace("f1")
        # _, trace3 = diags.get_objective_trace(obj)  # Can be removed
        np.testing.assert_array_equal(trace1, trace2)
        # np.testing.assert_array_equal(trace1, trace3)

Note that validate_vocs gets called in the generator standard base class. This makes it a bit difficult at the moment to incorporate the other functions like _check_parameters or _check_inputs as they work on the converted data types.

Small point. In test_ax_generators.py constraints use <= in Optimas, where as standard uses LESS_THAN.

outcome_constraints=["p1 <= 30"],

becomes:

constraints={"p1": ["LESS_THAN", 30.0]},

What do we do for test test_ax_single_fidelity_int. Do we need to support this?

    var1 = VaryingParameter("x0", -50.0, 5.0, dtype=int)
    var2 = VaryingParameter("x1", -5.0, 15.0)

In vocs this becomes:

    vocs = VOCS(
        variables={"x0": set(range(-50, 6)), "x1": [-5.0, 15.0]},
        objectives={"f": "MAXIMIZE"},
    )

I don't see anything to deal with discrete vars in Optimas, so I have converted to integer type when its a range but that seems hacky.

To support this elegantly, we could bring back IntegerVariable type in the vocs standard.