Mapping from parameters (i.e. a parameterization or parameter configuration) to parameter values. An arm provides the configuration to be tested in an Ax trial. Also known as "treatment group" or "parameterization", the name 'arm' comes from the Multi-Armed Bandit optimization problem, in which a player facing a row of “one-armed bandit” slot machines has to choose which machines to play when and in what order.
Single step in the experiment, contains multiple arms that are deployed and evaluated together. A batch trial is not just a trial with many arms; it is a trial for which it is important that the arms are evaluated simultaneously, e.g. in an A/B test where the evaluation results are subject to nonstationarity. For cases where multiple arms are evaluated separately and independently of each other, use multiple regular trials with a single arm each.
Relative outcome constraint
Configurable closed-loop optimization manager class, capable of conducting a full experiment by deploying trials, polling their results, and leveraging those results to generate and deploy more
trials (relevant tutorial).
Continuous, discrete or mixed design space that defines the set of parameters to be tuned in the optimization, and optionally parameter constraints on these parameters. The parameters of the arms to be evaluated in the optimization are drawn from a search space.
Standard error of the metric's mean, 0.0 for noiseless measurements. If no value is provided, defaults to
np.nan, in which case Ax infers its value using the measurements collected during experimentation.
Subclass of experiment that assumes synchronous evaluation (uses an evaluation function to get data for trials right after they are suggested). Abstracts away certain details, and allows for faster instantiation.