Comments (8)
@riyadparvez, in the short run you can get trials as ax_client.experiment.trials
, and you can get parameters from each one of the trials as trial.arm.parameters
. Will add this functionality to the service API, too, but that should get you unblocked for now.
from ax.
AxClient
class hasget_best_parameters()
method. I don't see any methods inAxClient
class that returns all the trials with their parameters and evaluation results. Is there any plans to add such API?
I think you can get the full results using the experiment object inside the client
ax = AxClient()
result = ax.experiment.fetch_data()
from ax.
This is what I see if I do print(result.df)
arm_name metric_name mean sem trial_index
0 0_0 hartmann6 -0.016126 0.0 0
1 0_0 l2norm 1.384382 0.0 0
2 1_0 hartmann6 -0.114288 0.0 1
3 1_0 l2norm 0.663507 0.0 1
4 2_0 hartmann6 -0.009832 0.0 2
5 2_0 l2norm 1.874537 0.0 2
Still no easy way to get the parameters and the metrics.
from ax.
@lena-kashtelyan thanks, it worked.
Is it also possible to expose which trial yielded the best results?
from ax.
Hello again, @riyadparvez –– currently get_best_parameters
outputs a tuple of parameterization and metric values; you can use the former as a workaround for now, to find which trial output the results.
get_best_parameters
is still somewhat under development; thank you for the good idea of adding the trial index to its output!
from ax.
This is now on master! You can do ax_client.get_trials_data_frame()
: )
from ax.
This functionality is now available in the latest stable version, 0.1.6!
from ax.
Thanks a lot! Will give it a try.
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Related Issues (20)
- MOO not respecting nonlinear constraints HOT 1
- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed HOT 1
- Safe optimization in the Service API HOT 5
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 5
- [GENERAL SUPPORT]: Plotting Pareto fronts / Posterior mean model HOT 6
- [GENERAL SUPPORT]: Getting best predicted point of a botorch model HOT 10
- [GENERAL SUPPORT]: Logical-or in outcome constraints HOT 4
- [GENERAL SUPPORT]: Parallelism and arbitrary parameter type support. HOT 5
- [GENERAL SUPPORT]: How to Standardize and Normalize Data for Service API HOT 6
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from ax.