Ehrlich functions

Ehrlich functions#

Type of objective function: discrete Environment to run this objective function: poli base

About#

Ehrlich functions, proposed by [Stanton et al., 2024], are a closed-form optimization objective for discrete sequences. They are maximized when a collection of motifs are fulfilled in the input. Check the details in their paper.

Prerequisites#

None, this black box runs out of the box.

If you want to run this black box directly (and you are not worried about conflicting dependencies), we recommend running

pip install "poli-core[ehrlich]"

How to run#

from poli.objective_repository import EhrlichHoloBlackBox, EhrlichHoloProblemFactory

# You can either
# (i) Create a black box
f = EhrlichHoloBlackBox(
    sequence_length=256,
    motif_length=8,
    n_motifs=4,
    quantization=8,
)

# or
# (ii) create a problem
problem = EhrlichHoloProblemFactory().create(
    sequence_length=256,
    motif_length=8,
    n_motifs=4,
    quantization=8,
)
f, x0 = problem.black_box, problem.x0

# Example input:
print(x0)

# Querying:
y = f(x0)
print(y)

How to cite#

[1] Stanton, S., Alberstein, R., Frey, N., Watkins, A., & Cho, K. (2024). Closed-form test functions for biophysical sequence optimization algorithms. arXiv. https://arxiv.org/abs/2407.00236

[2] González-Duque, M., Bartels, S., & Michael, R. (2024). poli: a libary of discrete sequence objectives [Computer software]. MachineLearningLifeScience/poli


@misc{Stanton:Ehrlich:2024,
      title={Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms}, 
      author={Samuel Stanton and Robert Alberstein and Nathan Frey and Andrew Watkins and Kyunghyun Cho},
      year={2024},
      eprint={2407.00236},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2407.00236}, 
}


@software{Gonzalez-Duque:poli:2024,
author = {González-Duque, Miguel and Bartels, Simon and Michael, Richard},
month = jan,
title = {{poli: a libary of discrete sequence objectives}},
url = {https://github.com/MachineLearningLifeScience/poli},
version = {0.0.1},
year = {2024}
}
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