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1. pietromani01

   added

   questionFurther information is requestedFurther information is requested

   on Mar 25, 2026
2. mgrange1998 commented on Mar 30, 2026

   

   mgrange1998

   on Mar 30, 2026

   Contributor

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   Hi Pietro,

   I took a look through and I believe you can get everything you need by accessing the descendent properties of the experiment objects. I summarized the call stack and the paths to the objects you'll need. From here just add the checks and log your tracked information trial by trial

   For your purposes to move through Client abstraction, you'll access things as follows:

   client.generation_strategy          # GenerationStrategy
     .adapter                          # Adapter (the "model bridge")
       .generator                      # BoTorchGenerator
         .surrogate.model              # fitted BoTorch GP model
         ._botorch_acqf_class          # resolved AcquisitionFunction class
   

   Code Pointers to Relevant Attributes

   • ax/service/ax_client.py — generation_strategy property
   • ax/generation_strategy/generation_strategy.py — .adapter property
   • ax/adapter/base.py — .generator attribute
   • ax/generators/torch/botorch_modular/generator.py — BoTorchGenerator (holds .surrogate_spec, ._botorch_acqf_class)
   • ax/generators/torch/botorch_modular/surrogate.py — Surrogate.model property → returns the fitted botorch.models.Model
   • ax/core/generator_run.py — gen_metadata property on GeneratorRun

   Suggestions

   1. Hyperparameter Evolution

   After calling get_next_trial() (which triggers a model fit), extract kernel hyperparameters:

   generator = client.generation_strategy.adapter.generator  # BoTorchGenerator
   botorch_model = generator.surrogate.model                 # fitted BoTorch Model
   

   With these, use standard BoTorch/GPyTorch inspection as needed:

   botorch_model.covar_module.base_kernel.lengthscale
   botorch_model.covar_module.outputscale
   botorch_model.likelihood.noise
   

   Log these after each get_next_trial() call to track evolution.

   2. Acquisition Function Values

   These are stored in gen_metadata on each GeneratorRun:

   trial = client.experiment.trials[trial_index]
   gr = trial.generator_run
   gr.gen_metadata  # dict with generation-time metadata
   

   3. Model Metadata (kernel + acqf identity)Copy

   gs = client.generation_strategy
   

   Which generation node you're on:

   gs.current_node_name
   

   The resolved acqf class:

   generator = gs.adapter.generator
   generator._botorch_acqf_class          # e.g. qLogExpectedImprovement
   generator.surrogate_spec               # SurrogateSpec with botorch_model_class
   

   The actual fitted model class:

   type(generator.surrogate.model)        # e.g. SingleTaskGP
   

   All of this is accessible after at least one BO trial has been generated (i.e., after the Sobol phase ends and the adapter is instantiated). Before that, gs.adapter will be None.

   Let me know if you get blocked on anything else, thank you again for using Ax!