evo-imp - Evolutionary Symbolic Imputer
A scikit-learn-compatible missing-value imputer for tabular data, powered
by DEAP. evo-imp evolves an interpretable
symbolic expression per feature with Genetic Programming, capturing
non-linear relationships that SimpleImputer/KNNImputer/IterativeImputer
cannot represent. It is a natural first step in the pipeline, upstream of
evo-gpfe (feature engineering) and evo-gafs
(feature selection).
Part of the evo-suite family (import name: evo_imp).
Documentation: https://evo-suite.readthedocs.io/
Why evo-imp?
| Capability | evo-imp |
|---|---|
| Interpretable, non-linear imputation: a symbolic expression per feature | Yes |
| Holdout-validated fitness: reported RMSE/fallback decision use a held-out split, not in-sample | Yes |
Never worse than the mean: automatic fallback when GP does not improve over SimpleImputer(mean) |
Yes |
Native scikit-learn transformer EvoImputer, usable in a Pipeline |
Yes |
Optional sympy-based expression simplification for readability |
Yes |
Built-in multi-dataset EvoImpBenchmarkRunner and MCAR evaluation helpers |
Yes |
Installation
pip install evo-imp # core
pip install "evo-imp[viz]" # + matplotlib for the plotting helpers
pip install "evo-imp[simplify]" # + sympy for simplify_expressions=True
Quickstart
from sklearn.datasets import load_diabetes
from evo_imp import EvoImpConfig, EvoImputer, introduce_mcar
X = load_diabetes(as_frame=True).data
X_missing, _ = introduce_mcar(X.values, missing_rate=0.20, random_seed=42)
imputer = EvoImputer(
config=EvoImpConfig(population_size=150, n_generations=30, verbose=False)
)
X_imputed = imputer.fit_transform(X_missing)
print(imputer.summary())
print(imputer.get_expressions())
Unlike the rest of evo-suite, EvoImputer is unsupervised (fit(X, y=None)) and its whole purpose is to accept NaN input — see the
Concepts guide
for why this is a deliberate exception to the family's usual scikit-learn
conventions.
Documentation & examples
- Full documentation (user guide + API reference): https://evo-suite.readthedocs.io/
- Runnable examples: the repository's
examples/evo-imp/directory.
Citation
@software{evo_imp,
author = {Skrauba, Axel},
title = {evo-imp: Evolutionary Symbolic Imputer for tabular data},
year = {2026},
version = {0.1.0},
url = {https://github.com/AxelSkrauba/evo-suite}
}
License
1 maintainer
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