No-priors characterization operator for sampling discovery spaces using high-dimensional sampling strategies
Project description
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/
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