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Bayesian Optimization for Precision Agriculture

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stair-lab/bo4ag

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Installation

  1. git clone https://github.com/stair-lab/bo4ag
  2. cd your_repository
  3. Create a python 3.10 conda environment
  4. pip install -r requirements.txt

Downloading the data:

  1. The coheritability data can be found on [huggingface](https://huggingface.co/datasets/stair-lab/coh2 or google drive.
  2. Download the files to ./Benchmark/data.

Running Bayesian Optimization (BO) Benchmarks

  1. cd Benchmarks
  2. Run python run_BO.py --env <environment>

Options:

Argument Description Default Value Example
--env Environment to run the search (e.g. narea, sla, pn, ps) None --env narea
--kernel Kernel function for the Gaussian process rbf --kernel matern52
--acq Acquisition function EI --acq EI
--transform Transforming on the search space None -- transform log
--n Number of iterations 300 --n 300
--gpu GPU id to run the job 0 --gpu 0
--run_name Name of the folder to move outputs None --run_name test

You can copy and paste this markdown table into your README.md file.

Example: python main.py --env narea --n 30 --kernel matern12 --acq EI --run_name test

Fitting A Surrogate Model

  1. cd Benchmarks
  2. Run python gp_fit.py --env <trait> --n <number of iterations> --kernel <kernel> --acq <acquisition function>

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