Flexible constrained random sampler for reverse analysis in machine learning
Project description
RandomSampler
mlsampler is a flexible, constraint-aware sampling library designed for reverse analysis and data exploration in machine learning workflows.
It allows you to generate synthetic samples that satisfy complex constraints across continuous, integer, binary, and categorical features.
Features
- Constraint-based sampling
- Support for continuous / categorical features
- Parallel generation
Documentation
https://jugais.github.io/randsampler/
Installation
pip install mlsampler
Use Cases
- Reverse analysis (finding inputs that satisfy target conditions)
- Candidate generation for optimization
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