eckity 0.2.3

EC-KitY: Evolutionary Computation Tool Kit in Python.

EC-KitY is a Python tool kit for doing evolutionary computation. It is scikit-learn-compatible and is distributed under the GNU General Public License v3.0. Currently we have implemented tree-based genetic programming (GP), but EC-KitY will grow!

EC-KitY is:

• A comprehensive toolkit for running evolutionary algorithms
• Written in Python
• Can work with or without scikit-learn, i.e., supports both sklearn and non-sklearn modes
• Designed with modern software engineering in mind
• Designed to support all popular EC paradigms (GA, GP, ES, coevolution, multi-objective, etc').

Dependencies

For the basic evolution mode, EC-KitY requires:

• numpy (>=1.14.6)
• pandas (>=0.25.0)
• overrides (>= 6.1.0)

For sklearn mode, EC-KitY additionally requires:

• scikit-learn (>=0.24.2)

User installation

pip install eckity

Documentation

API is available here

(Work in progress - some modules and functions are not documented yet.)

Tutorials

There are 4 tutorials available here, walking you through running EC-KitY both in sklearn mode and in non-sklearn mode.

Examples

More examples are in the examples folder. All you need to do is define a fitness-evaluation method, through a SimpleIndividualEvaluator sub-class.

Basic example (no sklearn)

You can run an EA with just 3 lines of code. The problem being solved herein is simple symbolic regression.

Additional information on this problem can be found in the Symbolic Regression Tutorial.

from eckity.algorithms.simple_evolution import SimpleEvolution
from eckity.subpopulation import Subpopulation
from examples.treegp.non_sklearn_mode.symbolic_regression.sym_reg_evaluator import SymbolicRegressionEvaluator

algo = SimpleEvolution(Subpopulation(SymbolicRegressionEvaluator()))
algo.evolve()
print(f'algo.execute(x=2,y=3,z=4): {algo.execute(x=2, y=3, z=4)}')

Example with sklearn

The problem being solved herein is the same problem, but in this case we also involve sklearn compatability - a core feature of EC-KitY. Additional information for this example can be found in the Sklearn Symbolic Regression Tutorial.

A simple sklearn-compatible EA run:

from sklearn.datasets import make_regression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

from eckity.algorithms.simple_evolution import SimpleEvolution
from eckity.creators.gp_creators.full import FullCreator
from eckity.genetic_encodings.gp.tree.utils import create_terminal_set
from eckity.sklearn_compatible.regression_evaluator import RegressionEvaluator
from eckity.sklearn_compatible.sk_regressor import SKRegressor
from eckity.subpopulation import Subpopulation

X, y = make_regression(n_samples=100, n_features=3)
terminal_set = create_terminal_set(X)

algo = SimpleEvolution(Subpopulation(creators=FullCreator(terminal_set=terminal_set),
                                     evaluator=RegressionEvaluator()))
regressor = SKRegressor(algo)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
regressor.fit(X_train, y_train)
print('MAE on test set:', mean_absolute_error(y_test, regressor.predict(X_test)))

Feature comparison

Here's a comparison table. The full paper is available here.

Authors

Moshe Sipper, Achiya Elyasaf, Itai Tzruia, Tomer Halperin

Citation

Citations are always appreciated 😊:

@article{eckity2022,
    author = {Sipper, Moshe and Halperin, Tomer and Tzruia, Itai and  Elyasaf, Achiya},
    title = {{EC-KitY}: Evolutionary Computation Tool Kit in {Python}},
    publisher = {arXiv},
    year = {2022},
    url = {https://arxiv.org/abs/2207.10367},
    doi = {10.48550/ARXIV.2207.10367},
}

@misc{eckity2022git,
    author = {Sipper, Moshe and Halperin, Tomer and Tzruia, Itai and  Elyasaf, Achiya},
    title = {{EC-KitY}: Evolutionary Computation Tool Kit in {Python}},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://www.eckity.org/} }
}