add methods

[lacava]

add SR methods for comparison. the following come to mind:

• FFX
• Feat
• EFS
• FEW
• TPOT? Not symbolic regression per se but interesting to include.
• https://github.com/ying531/MCMC-SymReg
• operon (added to dev)
• differentiable CGP
• gplearn

[jmmcd]

Another one that would be very nice is PGE (Worm & Chiu, GECCO 2013).

Paper: http://seminars.math.binghamton.edu/ComboSem/worm-chiu.pge_gecco2013.pdf
Code: https://github.com/verdverm/pypge

[lacava]

thanks, I'll reach out. doesn't look like it's being maintained.

[folivetti]

if I may, my algorithm was just accepted for publication:

Paper: https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00285
Code: https://github.com/folivetti/ITEA

Even though the code is in Haskell, I have included a Python wrapper in my repository, similar to your wrappers. Let me know if I can be of any help!

[lacava]

hi @folivetti , thanks for sharing. I'm going to be uploading a contributing guide soon that will detail how to include your method. Please stay tuned.

[lacava]

hi @folivetti, please see the contributing guide on the dev branch: https://github.com/EpistasisLab/regression-benchmark/blob/dev/CONTRIBUTING.md

Eventually this will be merged into master (still working on some hiccups with existing methods), but if you would like to start now, you can issue a PR to contribute your method to the dev branch. Let me know if you have any questions!

[folivetti]

thanks! I guess my code is already halfway through. As soon as I get the time to do so, I'll make the PR.

[folivetti]

I finally took the time to implement the python wrapper for ITEA. I have one final question: my code is written in Haskell using stack as the project manager. Should I include the installation of stack into the install script or should I put this requirement in a README file?

To install the stack environment you only need to run curl -sSL https://get.haskellstack.org/ | sh, but it may require a sudo permission since it installs GMP.

[lacava]

Sounds great. The entire install needs to be automated, so yes, any installation requirement needs to be implemented. When the code is set and you issue a PR, the repo is set up to test to make sure the installation passes and a mini benchmark runs without error.

…

On Sun, Mar 21, 2021, 4:40 PM Fabricio Olivetti de Franca < ***@***.***> wrote: I finally took the time to implement the python wrapper for ITEA. I have one final question: my code is written in Haskell using stack as the project manager. Should I include the installation of stack into the install script or should I put this requirement in a README file? To install the stack environment you only need to run curl -sSL https://get.haskellstack.org/ | sh, but it may require a sudo permission since it installs GMP. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#4 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABSGCGX7637QNJQDCWYQOUDTEZKSHANCNFSM4GYXGDNQ> .

[lacava]

To install the stack environment you only need to run curl -sSL https://get.haskellstack.org/ | sh, but it may require a sudo permission since it installs GMP.

sudo is ok

also wanted to mention you can test the install locally, doing something like

./configure
./install 
cd experiment
python -m pytest -v 

also see the github workflow for more info

[lacava]

@folivetti hope you got my email, but just checking if you think you'll have time to get ITEA integrated this week? many thanks!

[folivetti]

@folivetti hope you got my email, but just checking if you think you'll have time to get ITEA integrated this week? many thanks!

yes, I did receive the e-mail, thanks :-)
I have everything ready and should make the PR tomorrow. I'm just running some tests to double check that everything works. Thanks!

[gkronber]

I'm adding a few more methods for future reference.

• Equation Learner (EQL) https://github.com/martius-lab/EQL (already has Python code)
• SymbolicRegression.jl https://github.com/MilesCranmer/SymbolicRegression.jl
• Continued Fraction Regression (CFR, I reached out to the corresponding author https://www.sciencedirect.com/science/article/abs/pii/S0957417421004590)
• Universal Functions Originator (UFO, https://ieeexplore.ieee.org/abstract/document/9409797)
• AI Feynman [2.0] (https://advances.sciencemag.org/content/6/16/eaay2631)
• Feyn / QLattice Regression (https://arxiv.org/abs/2104.05417, https://arxiv.org/abs/2103.15147)
• "Accelerating symbolic regression with deep learning" (https://github.com/twhughes/Symbolic-Regression)
• Deep symbolic regression (https://arxiv.org/abs/1912.04871)

While it would be nice to have a transparent and objective way to compare all those methods it will probably be impossible to have all SymReg methods included into srbench for various reasons (e.g. closed source, difficulty to provide a Python wrapper, method is tuned to work well for certain problem characteristics, authors not cooperative, ...).

Researchers publishing SymReg methods should be made aware of srbench. I argue that we should be increasingly careful about new SymReg methods which are not included in srbench when reviewing or reading papers even when they are published in reputable journals.