Systematically searching the space of small directed, acyclic graphs (DAGs).
Published in the Paper: Scaling Up Unbiased Search-based Symbolic Regression, where it is named UDFS (Unbiased DAG Frame Search).
Option 1
Clone the repository and then follow
conda create --name testenv python=3.9.12
conda activate testenv
pip install -r requirements.txt
... do stuff here
conda deactivate
conda remove -n testenv --all
Option 2
Copy the install script install.sh and then run
bash install.sh
Lets consider a regression problem with N samples of inputs X (shape N x m) and outputs y (shape N).
Estimation of an expression is as simple as this:
from DAG_search import dag_search
est = dag_search.DAGRegressor()
est.fit(X, y)
The fitted expression can then be accessed via
est.model()
Note that the model is returned as a sympy expression.
For prediction simply use
pred = est.predict(X)
or
pred, grad = est.predict(X, return_grad = True)
For advanced usage see the Tutorial-Notebook tutorial.ipynb.
To reference this work, please use the following citation:
@inproceedings{Kahlmeyer:IJCAI24,
title = {Scaling Up Unbiased Search-based Symbolic Regression},
author = {Kahlmeyer, Paul and Giesen, Joachim and Habeck, Michael and Voigt, Henrik},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {4264--4272},
year = {2024},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2024/471},
url = {https://doi.org/10.24963/ijcai.2024/471},
}