| Name | bingo-nasa JSON |
| Version |
0.5.4
JSON |
| download |
| home_page | None |
| Summary | A package for genetic optimization and symbolic regression. |
| upload_time | 2025-09-04 19:36:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
symbolic
regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
mpi4py
numpy
scipy
dill
sympy
scikit-learn
smcpy
pybind11
onnx
pytest
pytest-mock
pytest-timeout
pytest-cov
coverage
coveralls
|
| Travis-CI |
|
| coveralls test coverage |
|
master: [](https://github.com/nasa/bingo/actions?query=branch%3Adevelop)
[](https://coveralls.io/github/nasa/bingo?branch=develop)
develop:
[](https://github.com/nasa/bingo/actions?query=branch%3Adevelop)
[](https://coveralls.io/github/nasa/bingo?branch=develop)
## Description
Bingo is an open source package for performing symbolic regression, though it
can be used as a general purpose evolutionary optimization package.
## Key Features
* Integrated local optimization strategies
* Parallel island evolution strategy implemented with mpi4py
* Coevolution of fitness predictors
# Quick Start
## Documentation
[Full Documentation Here](https://nasa.github.io/bingo/)
## Installation
```sh
pip install bingo-nasa
```
## Usage Example
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.
### Setting Up the Regressor
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](https://nasa.github.io/bingo/_apidocs/bingo.symbolic_regression.html#module-bingo.symbolic_regression.symbolic_regressor).
```python
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}")
### Training Data
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](https://nasa.github.io/bingo/_high_level/data_formatting.html).
```python
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
```
```python
import matplotlib.pyplot as plt
plt.scatter(X, y)
plt.xlabel("X_0")
plt.ylabel("y")
plt.title("Training Data")
plt.show()
```
### Fitting the Regressor
Fitting is as simple as calling the `.fit()` method.
```python
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
### Getting the Best Individual
```python
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))
### Predicting Data with the Best Individual
You can use the regressor's `.predict(X)` or
the best_individual's `.evaluate_equation_at(X)` to get
its predictions for `X`.
```python
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()
```
# Source
## Installation from Source
For those looking to develop their own features in Bingo.
First clone the repo and move into the directory:
```sh
git clone --recurse-submodules https://github.com/nasa/bingo.git
cd bingo
```
Then make sure you have the requirements necessary to use Bingo:
```sh
conda env create -f conda_environment.yml
```
or
```sh
pip install -r requirements.txt
```
(Optional) Then build the c++ performance library BingoCpp:
```sh
./.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:
```sh
pytest tests
```
Add Bingo to your Python path to begin using it from other directories.
```sh
export PYTHONPATH="$PYTHONPATH:/path/to/bingo/"
```
and test it with:
```sh
python -c 'import bingo; import bingocpp'
```
## Contributing
1. Fork it (<https://github.com/nasa/bingo/fork>)
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Create a new Pull Request
# Citing Bingo
Please consider citing the following reference when using bingo in your works.
### MLA:
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.
### Bibtex:
```latex
@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}
}
```
# Versioning
We use [SemVer](http://semver.org/) for versioning. For the versions available,
see the [tags on this repository](https://github.com/nasa/bingo/tags).
# Authors
* Geoffrey Bomarito
* Tyler Townsend
* Jacob Hochhalter
* David Randall
* Ethan Adams
* Kathryn Esham
* Diana Vera
# License
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.
Raw data
{
"_id": null,
"home_page": null,
"name": "bingo-nasa",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "symbolic regression",
"author": null,
"author_email": "Geoffrey Bomarito <geoffrey.f.bomarito@nasa.gov>",
"download_url": "https://files.pythonhosted.org/packages/35/eb/9fad83b6ab1b9359c35fc94ef0e1c3d6b274025fc16ba45fca302fef155d/bingo_nasa-0.5.4.tar.gz",
"platform": null,
"description": "\n\nmaster: [](https://github.com/nasa/bingo/actions?query=branch%3Adevelop)\n[](https://coveralls.io/github/nasa/bingo?branch=develop) \n\ndevelop: \n[](https://github.com/nasa/bingo/actions?query=branch%3Adevelop)\n[](https://coveralls.io/github/nasa/bingo?branch=develop) \n\n## Description\nBingo is an open source package for performing symbolic regression, though it \ncan be used as a general purpose evolutionary optimization package. \n\n## Key Features\n* Integrated local optimization strategies\n* Parallel island evolution strategy implemented with mpi4py\n* Coevolution of fitness predictors\n\n# Quick Start\n\n## Documentation\n[Full Documentation Here](https://nasa.github.io/bingo/)\n\n## Installation\n\n```sh\npip install bingo-nasa\n```\n\n## Usage Example\nA no-fuss way of using Bingo is by using the scikit-learn wrapper:\n`SymbolicRegressor`. Let's setup a test case to show how it works.\n\n### Setting Up the Regressor\n\nThere are many options that can be set in `SymbolicRegressor`. Here we set some basic ones including\n`population_size` (the number of equations in a population), `stack_size` (the max number of nodes per equation), and `use_simplification`\n(whether to use simplification to speed up equation evaluation and for easier reading). You can see all of `SymbolicRegressor`'s\noptions [here](https://nasa.github.io/bingo/_apidocs/bingo.symbolic_regression.html#module-bingo.symbolic_regression.symbolic_regressor).\n\n\n```python\nfrom bingo.symbolic_regression.symbolic_regressor import SymbolicRegressor\nregressor = SymbolicRegressor(population_size=100, stack_size=16,\n use_simplification=True)\n```\n\n /home/gbomarit/Projects/Genetic_Programming/bingo/bingo/symbolic_regression/__init__.py:31: UserWarning: Could not load C++ modules No module named 'bingocpp.build.bingocpp'\n warnings.warn(f\"Could not load C++ modules {import_err}\")\n\n\n### Training Data\nHere 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\nin the [data formatting guide](https://nasa.github.io/bingo/_high_level/data_formatting.html).\n```python\nimport numpy as np\nX_0 = np.linspace(-10, 10, num=30).reshape((-1, 1))\nX = np.array(X_0)\ny = 5.0 * X_0 ** 2 + 3.5 * X_0\n```\n\n\n```python\nimport matplotlib.pyplot as plt\nplt.scatter(X, y)\nplt.xlabel(\"X_0\")\nplt.ylabel(\"y\")\nplt.title(\"Training Data\")\nplt.show()\n```\n\n\n \n\n \n\n\n### Fitting the Regressor\n\nFitting is as simple as calling the `.fit()` method.\n\n\n```python\nregressor.fit(X, y)\n```\n\n using 1 processes\n Generating a diverse population took 274 iterations.\n archipelago: <class 'bingo.evolutionary_optimizers.island.Island'>\n done with opt, best_ind: X_0 + (5.0)((0.49999999999999967)(X_0) + (X_0)(X_0)), fitness: 5.4391466376923e-28\n reran CLO, best_ind: X_0 + (5.0)((0.4999999999999999)(X_0) + (X_0)(X_0)), fitness: 5.352980018399097e-28\n\n\n### Getting the Best Individual\n\n\n```python\nbest_individual = regressor.get_best_individual()\nprint(\"best individual is:\", best_individual)\n```\n\n best individual is: X_0 + (5.0)((0.4999999999999999)(X_0) + (X_0)(X_0))\n\n\n### Predicting Data with the Best Individual\n\nYou can use the regressor's `.predict(X)` or\nthe best_individual's `.evaluate_equation_at(X)` to get\nits predictions for `X`.\n\n\n```python\npred_y = regressor.predict(X)\npred_y = best_individual.evaluate_equation_at(X)\n\nplt.scatter(X, y)\nplt.plot(X, pred_y, 'r')\nplt.xlabel(\"X_0\")\nplt.ylabel(\"y\")\nplt.legend([\"Actual\", \"Predicted\"])\nplt.show()\n```\n\n\n \n\n\n# Source\n\n## Installation from Source\n\nFor those looking to develop their own features in Bingo.\n\nFirst clone the repo and move into the directory:\n\n```sh\ngit clone --recurse-submodules https://github.com/nasa/bingo.git\ncd bingo\n```\n\nThen make sure you have the requirements necessary to use Bingo:\n\n\n```sh\nconda env create -f conda_environment.yml\n```\n\nor\n\n```sh\npip install -r requirements.txt\n```\n\n(Optional) Then build the c++ performance library BingoCpp:\n\n```sh\n./.build_bingocpp.sh\n```\n\nNow you should be good to go! You can run Bingo's test suite to make sure that\nthe installation process worked properly:\n\n```sh\npytest tests\n```\n\nAdd Bingo to your Python path to begin using it from other directories.\n\n```sh\nexport PYTHONPATH=\"$PYTHONPATH:/path/to/bingo/\"\n```\n\nand test it with:\n\n```sh\npython -c 'import bingo; import bingocpp'\n```\n\n## Contributing\n1. Fork it (<https://github.com/nasa/bingo/fork>)\n2. Create your feature branch (`git checkout -b feature/fooBar`)\n3. Commit your changes (`git commit -am 'Add some fooBar'`)\n4. Push to the branch (`git push origin feature/fooBar`)\n5. Create a new Pull Request\n\n# Citing Bingo\nPlease consider citing the following reference when using bingo in your works.\n\n### MLA:\nRandall, David L., et al. \"Bingo: a customizable framework for symbolic regression with genetic programming.\" Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2022.\n\n### Bibtex:\n```latex\n@inproceedings{randall2022bingo,\n title={Bingo: a customizable framework for symbolic regression with genetic programming},\n author={Randall, David L and Townsend, Tyler S and Hochhalter, Jacob D and Bomarito, Geoffrey F},\n booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},\n pages={2282--2288},\n year={2022}\n}\n```\n\n# Versioning\nWe use [SemVer](http://semver.org/) for versioning. For the versions available, \nsee the [tags on this repository](https://github.com/nasa/bingo/tags). \n\n# Authors\n* Geoffrey Bomarito\n* Tyler Townsend\n* Jacob Hochhalter\n* David Randall\n* Ethan Adams\n* Kathryn Esham\n* Diana Vera\n \n# License \nCopyright 2018 United States Government as represented by the Administrator of \nthe National Aeronautics and Space Administration. No copyright is claimed in \nthe United States under Title 17, U.S. Code. All Other Rights Reserved.\n\nThe Bingo Mini-app framework is licensed under the Apache License, Version 2.0 \n(the \"License\"); you may not use this application except in compliance with the \nLicense. You may obtain a copy of the License at \nhttp://www.apache.org/licenses/LICENSE-2.0 .\n\nUnless required by applicable law or agreed to in writing, software distributed \nunder the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR \nCONDITIONS OF ANY KIND, either express or implied. See the License for the \nspecific language governing permissions and limitations under the License.\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. 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