Bingo is an open source package for performing symbolic regression, though it can be used as a general purpose evolutionary optimization package.
To install Bingo, simply use pip. Unfortunately the pip install is currently not working. Please use source install for the time being.
pip install bingo-nasa
A no-fuss way of using Bingo is by using the scikit-learn wrapper:
SymbolicRegressor
. Let's setup a test case to show how it works.
There are many options that can be set in SymbolicRegressor
. Here we set some basic ones including
population_size
(the number of equations in a population), stack_size
(the max number of nodes per equation), and use_simplification
(whether to use simplification to speed up equation evaluation and for easier reading). You can see all of SymbolicRegressor
's
options here.
from bingo.symbolic_regression.symbolic_regressor import SymbolicRegressor
regressor = SymbolicRegressor(population_size=100, stack_size=16,
use_simplification=True)
/home/gbomarit/Projects/Genetic_Programming/bingo/bingo/symbolic_regression/__init__.py:31: UserWarning: Could not load C++ modules No module named 'bingocpp.build.bingocpp'
warnings.warn(f"Could not load C++ modules {import_err}")
Here we're just creating some dummy training data from the equation $5.0 X_0^2 + 3.5 X_0$. More on training data can be found in the data formatting guide.
import numpy as np
X_0 = np.linspace(-10, 10, num=30).reshape((-1, 1))
X = np.array(X_0)
y = 5.0 * X_0 ** 2 + 3.5 * X_0
import matplotlib.pyplot as plt
plt.scatter(X, y)
plt.xlabel("X_0")
plt.ylabel("y")
plt.title("Training Data")
plt.show()
Fitting is as simple as calling the .fit()
method.
regressor.fit(X, y)
using 1 processes
Generating a diverse population took 274 iterations.
archipelago: <class 'bingo.evolutionary_optimizers.island.Island'>
done with opt, best_ind: X_0 + (5.0)((0.49999999999999967)(X_0) + (X_0)(X_0)), fitness: 5.4391466376923e-28
reran CLO, best_ind: X_0 + (5.0)((0.4999999999999999)(X_0) + (X_0)(X_0)), fitness: 5.352980018399097e-28
best_individual = regressor.get_best_individual()
print("best individual is:", best_individual)
best individual is: X_0 + (5.0)((0.4999999999999999)(X_0) + (X_0)(X_0))
You can use the regressor's .predict(X)
or
the best_individual's .evaluate_equation_at(X)
to get
its predictions for X
.
pred_y = regressor.predict(X)
pred_y = best_individual.evaluate_equation_at(X)
plt.scatter(X, y)
plt.plot(X, pred_y, 'r')
plt.xlabel("X_0")
plt.ylabel("y")
plt.legend(["Actual", "Predicted"])
plt.show()
For those looking to develop their own features in Bingo.
First clone the repo and move into the directory:
git clone --recurse-submodules https://github.com/nasa/bingo.git
cd bingo
Then make sure you have the requirements necessary to use Bingo:
pip install -r requirements.txt
or
conda install --channel conda-forge --file requirements.txt
(Optional) Then build the c++ performance library BingoCpp:
./.build_bingocpp.sh
Now you should be good to go! You can run Bingo's test suite to make sure that the installation process worked properly:
pytest tests
Add Bingo to your Python path to begin using it from other directories.
export PYTHONPATH="$PYTHONPATH:/path/to/bingo/"
and test it with:
python -c 'import bingo; import bingocpp'
git checkout -b feature/fooBar
)git commit -am 'Add some fooBar'
)git push origin feature/fooBar
)Please consider citing the following reference when using bingo in your works.
Randall, David L., et al. "Bingo: a customizable framework for symbolic regression with genetic programming." Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2022.
@inproceedings{randall2022bingo,
title={Bingo: a customizable framework for symbolic regression with genetic programming},
author={Randall, David L and Townsend, Tyler S and Hochhalter, Jacob D and Bomarito, Geoffrey F},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages={2282--2288},
year={2022}
}
We use SemVer for versioning. For the versions available, see the tags on this repository.
Copyright 2018 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.
The Bingo Mini-app framework is licensed under the Apache License, Version 2.0 (the "License"); you may not use this application except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 .
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.