Manually Sampling Specific Parameterizations

While Ax excels at generating high-quality trials using Bayesian optimization, we may wish to attach trials with specific parameterizations to their experiment.

Introduction

This could be because we have an intuition that certain parameterizations will be successful and want to encourage the optimizer to explore this region of the search space. It could be that we have some “baseline” configuration which represents the status quo of our system.

In any of these cases, we can use the Client's attach_trial method to manually attach a trial with a custom parameterization to our experiment.

Prerequisites

We will assume you are already familiar with basic Ax usage.

Setup

Before we begin you must instantiate the Client and configure it your your experiment.

client = Client()

client.configure_experiment(...)
client.configure_optimization(...)

Steps

1. Call attach_trial with your custom parameterization
2. Attach data and complete the trial as you typically would

1. Call attach_trial with your custom parameterization

attach_trial takes two parameters: the parameters of the trial and optionally an arm name (here, arm is synonymous with trial). The parameters dictionary must contain a value for each parameter in the experiment's search space. The arm name is not necessary, but it can be useful for keeping track of which arms were not generated by Ax when looking at analyses. Once called attach_trial will return the newly created trial's index -- we'll need this when we want to attach data and complete the trial.

my_parameters = {"x1": 0, "x2": 0, "x3": 0, "x4": 0, "x5": 0, "x6": 0}
trial_index = client.attach_trial(
    parameters=my_parameters,
    arm_name="origin",
)

2. Attach data and complete the trial as you typically would

Once the trial index has been generated you may evaluate the trial and tell the results to Ax using complete_trial.

client.complete_trial(
    trial_index=trial_index,
    raw_data={"hartmann6": hartmann6(**my_parameters)}
)