Comments (5)
@yizhanKT Yes, then you'll still need to run it, and fetch data/complete it as you do for other trials.
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Hi @yizhanKT , I think by "user input default parameter value set" you mean what we commonly refer to as the status quo.
I'm trying to figure out if there is a more canonical way to do this, but if you're using the service API tutorial you can always add a status quo to the experiment and manually attach that trial
# attach status quo on experiment creation
ax_client.create_experiment(
....
# substitute your parameters here
status_quo={"x1": 0.0, "x2": 0.0, "x3": 0.0, "x4": 0.0, "x5": 0.0, "x6": 0.0},
}
# alternatively you can set it later
ax_client.set_status_quo(params={"x1": 0.0, "x2": 0.0, "x3": 0.0, "x4": 0.0, "x5": 0.0, "x6": 0.0})
# you can then manually create the status quo trial and evaluate it
parameters, trial_index = ax_client.attach_trial(
parameters=ax_client.status_quo,
# it's probably the status quo
arm_name="status_quo",
)
ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))
# then continue with the code already in the tutorial to get trials from the generation strategy
for i in range(25):
parameters, trial_index = ax_client.get_next_trial()
# Local evaluation here can be replaced with deployment to external system.
ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))
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@danielcohenlive Thank you for your reply! yes, the status quo arm is the exact function I was looking for. Just want to confirm, as I am currently using develop API, in this case, I shall just append an Arm to the AX experiment as the following?
# If only one arm should be evaluated
experiment.new_trial().add_arm(Arm(parameters={"x1": 0.0, "x2": 0.0, "x3": 0.0, "x4": 0.0, "x5": 0.0, "x6": 0.0}, name="status_quo"))
from ax.
Got it, thank you!
from ax.
I'm closing this issue, but feel to reopen or open a new one if you have further questions
from ax.
Related Issues (20)
- Trouble with searching documentation (Algolia, API docs, etc.), for example `get_next_trials` HOT 1
- Issue when starting an AxClient with out-of-design points HOT 2
- cannot import name 'TrainingData' HOT 2
- applying complex constrains HOT 2
- Ax is not not starting as many workers as I'd like to; sometimes, get_next_trials returns 0 new trials HOT 4
- Evaluating custom candidates HOT 2
- Input Feature Selection - Does the relevant code exist? HOT 6
- [Feature Request] support constraints on `ChoiceParameters` HOT 4
- Extending Models.THOMPSON with an extra parameter HOT 1
- There are some questions when i use the Ax HOT 7
- Space characters in the objective name AND specifying a threshold leads to an error message: "AssertionError: Outcome constraint should be of form `metric_name >= x" HOT 1
- Pandas deprecation warning when deserializing AxClient JSON HOT 2
- AX seems to get stuck with Ray
- `StandardizeY` transform requires non-empty data." when using SAASBO
- Plotting outside of a notebook HOT 1
- Setting search space step size in Ax Service API HOT 10
- Problem when Sobol falls back to HitAndRunPolytopeSampler HOT 3
- Arms from previous batch keep appearing in new batches HOT 5
- EHVI & NEHVI break with more than 7 objectives HOT 4
- Multi-objective experiments generate duplicated data HOT 5
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