ADO No-Priors Characterization Operator

ado-no-priors-characterization is an operator plugin for the Accelerated Discovery Orchestrator (ADO), providing initial exploration of discovery spaces using high-dimensional sampling strategies.

No-Priors Characterization is designed for unbiased exploration when no measured data exists yet, establishing an initial dataset for subsequent model-based exploration.

How it Works

The No-Priors Characterization operator uses different sampling strategies to ensure good coverage of the discovery space:

• random: Random sampling across the space for unbiased exploration. This provides the baseline sampling approach.
• clhs (Concatenated Latin Hypercube Sampling): Ensures uniform coverage by enforcing stratification in each dimension independently. Each dimension cycles through all possible values before repeating.
• sobol: Sobol sequence sampling for quasi-random low-discrepancy coverage

The operator retrieves already-measured entities from the discovery space, orders the unmeasured entities using the specified sampling strategy, and yields entities in batches for measurement.

Installation

You can install the No-Priors Characterization operator and its dependencies (including ado-core) directly from PyPI:

pip install ado-no-priors-characterization

More Information

To learn more about No-Priors Characterization and explore the full capabilities of ADO, including detailed documentation, configuration guides, and additional examples, visit the official ADO website:

• No-Priors Quickstart: https://ibm.github.io/ado/examples/no-priors-characterization/
• Configuring No-Priors: https://ibm.github.io/ado/operators/no-priors-characterization/
• ADO Documentation: https://ibm.github.io/ado/