PyePAL: Pareto active learning for Python¶
This package implements an active learning approach to efficiently and confidently identify the Pareto front with any regression model that can output a mean and a standard deviation.
It works with any number of objectives, missing data, and is highly customizable.
If you find this code useful for your work, please cite:
Contents¶
- Getting Started
- Background
- Tutorials
- The PyePAL API reference
- The PAL package
- Core functions
- Base class
- For GPy models
- For coregionalized GPy models
- For sklearn GPR models
- For quantile regression with LightGBM
- For GPR with GPFlow
- For GPR with BoTorch
- For GBDT with CatBoost
- Schedules for hyperparameter optimization
- Utilities for multiobjective optimization
- Utilities for plotting
- Input validation
- The models package
- The PAL package
- Developer notes