https://github.com/blakeaw/galibrate
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
https://github.com/blakeaw/galibrate
genetic-algorithm model-calibration optimization optimizer parameter-estimation pysb python3 systems-biology
Last synced: 5 days ago
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Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
- Host: GitHub
- URL: https://github.com/blakeaw/galibrate
- Owner: blakeaw
- License: mit
- Created: 2019-07-17T01:55:31.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-08-16T18:31:08.000Z (9 months ago)
- Last Synced: 2025-05-11T18:16:02.445Z (9 days ago)
- Topics: genetic-algorithm, model-calibration, optimization, optimizer, parameter-estimation, pysb, python3, systems-biology
- Language: Jupyter Notebook
- Homepage:
- Size: 396 KB
- Stars: 12
- Watchers: 3
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.cff
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README
# GAlibrate
[](LICENSE)
[](https://github.com/blakeaw/GAlibrate/releases/tag/v0.7.2)
[](https://anaconda.org/blakeaw/galibrate)
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[](https://github.com/psf/black)
[](https://zenodo.org/badge/latestdoi/197295657)
**GAlibrate** is a python toolkit that provides an easy to use interface for model calibration/parameter estimation using an implementation of continuous genetic algorithm-based optimization. Its functionality and API were designed to be familiar to users of the [PyDREAM](https://github.com/LoLab-VU/PyDREAM), [simplePSO](https://github.com/LoLab-VU/ParticleSwarmOptimization), and [Gleipnir](https://github.com/LoLab-VU/Gleipnir) packages.
Although **GAlibrate** provides a general framework for running continuous
genetic algorithm-based optimizations, it was created with systems biology models in mind. It therefore supplies additional tools for working with biological models in the [PySB](http://pysb.org/) format.
### What's new in
#### version 0.7.0
* Julia integration - New version of core GA that ports some key funtions to Julia using the PyJulia package.
* New `benchmarks` module defining a set of functions used to benchmark and test single objective optimazation routines.
* Test suite using pytest with 63% overall coverage.
* Updated profiling and performance benchmarking Jupyter [notebooks](./notebooks/).
* Function to resume/continue GAO runs for additional generations: `GAO.resume`.
* Several new example cases under [examples](./examples/)
#### version 0.6.0
* core GA now returns an array with fitness value of the fittest individual from each generation which can be accessed from the GAO property `GAO.best_fitness_per_generation`.
* Bug fix in core GA for sorting the population before selection and mating.
#### version 0.5.0
* Optional progress bar to monitor passage of generations during GAO run that is only displayed if [tqdm](https://github.com/tqdm/tqdm) is installed
* Optional [multiprocessing](https://docs.python.org/2/library/multiprocessing.html) based parallelism when evaluating the fitness function over the population during a GAO run.
## Table of Contents
1. [Install](#install)
1. [pip install](#pip-install)
2. [conda install](#conda-install)
3. [Recomended additional software](#recomended-additional-software)
2. [License](#license)
3. [Change Log](#change-log)
4. [Documentation and Usage](#documentation-and-usage)
1. [Quick Overview](#quick-overview)
2. [Examples](#examples)
5. [Contact](#contact)
------
# Install
| **! Note** |
| :--- |
| GAlibrate is still in version zero development so new versions may not be backwards compatible. |
**GAlibrate** installs as the `galibrate` package. It is compatible (i.e., tested) with Python 3.10.11.
Note that `galibrate` has the following core dependencies:
* [NumPy](http://www.numpy.org/)
* [SciPy](https://www.scipy.org/)
### pip install
You can install the latest release of the `galibrate` package using `pip` sourced from the GitHub repo -
**Fresh install:**
```
pip install https://github.com/blakeaw/GAlibrate/archive/refs/tags/v0.7.2.zip
```
**Or to upgrade from an older version:**
```
pip install --upgrade https://github.com/blakeaw/GAlibrate/archive/refs/tags/v0.7.2.zip
```
### PyPI
`galibrate` can also be `pip` installed from PyPI,
```
pip install galibrate
```
but this version currently doesn't include the Cython accelerated version of the core GA algorithm.
### conda
You can install the `galibrate` package from the `blakeaw` channel:
```
conda install -c blakeaw galibrate
```
NumPy and SciPy dependencies will be automatically installed with this version.
### Recommended additional software
The following software is not required for the basic operation of **GAlibrate**, but provides extra capabilities and features when installed.
#### Cython
**GAlibrate** includes an implementation of the core genetic algorithm that is written in [Cython](https://cython.org/), which takes advantage of Cython-based optimizations and compilation to accelerate the algorithm. This version of genetic algorithm is used if Cython is installed.
#### Numba
**GAlibrate** also includes an implementation of the core genetic algorithm that takes advantage of [Numba](https://numba.pydata.org/)-based JIT compilation and optimization to accelerate the algorithm. This version of genetic algorithm is used if Numba is installed.
#### Julia
**GAlibrate** also includes an implementation of the core genetic algorithm that takes advantage of porting some key functions to [Julia](https://julialang.org/) for JIT compilation and optimization to accelerate the algorithm. This version of genetic algorithm requires [Julia](https://julialang.org/) and [PyJulia](https://pyjulia.readthedocs.io/en/latest/); note that the Python-based CLI tool [jill](https://pypi.org/project/jill/) is also an option for automating the process of downloading and installing Julia.
#### tqdm
GAO runs will display a progress bar that tracks the passage of generations when the [tqdm](https://github.com/tqdm/tqdm) package installed.
#### PySB
[PySB](http://pysb.org/) is needed to run PySB models, and it is therfore needed if you want to use tools from the `galibrate.pysb`` package.
------
### Testing
Tests and coverage analysis use
* [pytest](https://docs.pytest.org/en/stable/) (`pytest=7.4.0`)
* [Coverage.py](https://coverage.readthedocs.io/en/7.6.0/) (`coverage=7.2.2`)
Running locally from the GAlibrate repo folder:
```
coverage run -m pytest
```
then to see coverage report:
```
coverage report -m
```
# License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
------
# Change Log
See: [CHANGELOG](CHANGELOG.md)
------
# Documentation and Usage
### Quick Overview
Principally, **GAlibrate** defines the **GAO** (continuous **G**enetic **A**lgorithm-based **O**ptimizer ) class,
```python
from galibrate import GAO
```
which defines an object that can be used setup and run a continuous genetic algorithm-based optimization (i.e., a maximization) of a user-defined fitness function over the search space of a given set of (model) parameters.
#### multiprocessing-based parallelism
The multiprocessing-based parallelism (single node) can be invoked by passing the keyword argument `nprocs` with a value greater than one when calling the `GAO.run` function; for example, `gao.run(nprocs=2)` will use two processes. A full example is provided in [this script](./examples/pysb_dimerization_model/galibrate_dimerization_model_parallel.py).
Parallelism is used when evaluating the fitness function across the population (whole population during initialization and half the population during subsequent generations). You can expect the most parallel speedup when the fitness function is expensive to evaluate, such as when evaluating a PySB model. You may also get speedup when the population is very large, depending on how expensive the fitness function is to evaluate. Note however, that if the fitness function is fast to evaluate then the parallel overhead may actually slow down the run.
#### PySB models
Additionally, **GAlibrate** has a `pysb` sub-package that provides the
`galibrate_it` module, which defines the GaoIt and GAlibrateIt classes (importable from the `galibrate.pysb` package level),
```python
from galibrate.pysb import GaoIt, GAlibrateIt
```
which create objects that abstract away some of the effort to setup and generate GAO instances for PySB models; [examples/pysb_dimerization_model](./examples/pysb_dimerization_model) provides some
examples for using GaoIt and GAlibrateIt objects. The `galibrate_it` module can also be called from the command line to generate a template run script for a PySB model,
```python
python -m galibrate.pysb_utils.galibrate_it pysb_model.py output_path
```
which users can then modify to fit their needs.
### Examples
Additional example scripts that show how to setup and launch Genetic Algorithm runs using **GAlibrate** can be found under [examples](./examples).
------
# Contact
### For Support
Email support inquiries to [[email protected]](mailto:[email protected])
### Interested in contributing
See [CONTRIBUTING](./CONTRIBUTING.md)
------