evo-gpfe 0.1.0

Genetic Programming Feature Engineer — a scikit-learn-compatible symbolic feature constructor for tabular data (evo-suite)

evo-gpfe - Genetic Programming Feature Engineer

A scikit-learn-compatible symbolic feature constructor for tabular data, powered by DEAP. evo-gpfe evolves expression trees that combine the original features into new, informative ones, complementing evo-gafs (which selects among existing features).

Part of the evo-suite family (import name: evo_gpfe). Documentation: https://evo-suite.readthedocs.io/

Why evo-gpfe?

Capability	evo-gpfe
Sequential hall-of-fame strategy: relevance minus redundancy minus parsimony	Yes
Protected primitives (safe division, log, sqrt, ...) so any random tree evaluates	Yes
Multiple relevance metrics: mutual_info, correlation, spearman, model_score	Yes
Anti-bloat: static tree-height limit and a parsimony penalty	Yes
Native scikit-learn fit/transform/get_feature_names_out, usable in a Pipeline	Yes
Built-in multi-dataset GPBenchmarkRunner	Yes

Installation

pip install evo-gpfe            # core
pip install "evo-gpfe[viz]"     # + matplotlib for the plotting helpers

Quickstart

from sklearn.datasets import load_diabetes
from evo_gpfe import GPFeatureEngineer, GPConfig

X, y = load_diabetes(return_X_y=True, as_frame=True)

engineer = GPFeatureEngineer(
    config=GPConfig(population_size=80, n_generations=20, n_features_to_generate=3, verbose=False)
)
engineer.fit(X, y)

print(engineer.summary())
X_augmented = engineer.transform(X)
print("Output features:", list(engineer.get_feature_names_out()))

The GP-then-GA combo

evo-gpfe and evo-gafs are independent but complementary: construct new features with GP, then select the best subset (original + generated) with GA.

from evo_gafs import GAFeatureSelector, GAConfig

X_aug = engineer.fit_transform(X, y)
selector = GAFeatureSelector(estimator=..., feature_names=list(engineer.get_feature_names_out()))
selector.fit(X_aug, y)

See 06_gp_then_ga_pipeline.py in the repository's examples/evo-gpfe/ directory for the full example.

Documentation & examples

• Full documentation (user guide + API reference): https://evo-suite.readthedocs.io/
• Runnable examples: the repository's examples/evo-gpfe/ directory.

Citation

@software{evo_gpfe,
  author    = {Skrauba, Axel},
  title     = {evo-gpfe: Genetic Programming Feature Engineer for tabular data},
  year      = {2026},
  version   = {0.1.0},
  url       = {https://github.com/AxelSkrauba/evo-suite}
}

License

MIT