pyensemblefs: a multi-threading Python library for ensemble feature selection.

This repository hosts pyensemblefs, a Python library for ensemble feature selection. Supports heterogeneous ensembles, bootstrapped evaluation, and stability analysis across feature selectors.
It assists researchers in feature selection tasks without requiring significant programming effort.

Installation and setup

pip install pyensemblefs

To download the source code, you can clone it from the GitHub repository:

git clone git@github.com:cdchushig/pyensemblefs.git

Requirements: Python ≥ 3.9, scikit-learn ≥ 1.2

Library Highlights

pyensemblefs automatically extracts relevant features in datasets using bootstrapping and ensemble aggregation.

• Intuitive, reproducible workflows: compatible with scikit-learn pipelines.
• Comprehensive documentation: each feature selection and aggregation method is fully described.
• Extensible architecture: easily add custom selection or aggregation strategies.

Main Features

• Bootstrap-based ensemble selection: assess variability across resamples.

• Heterogeneous ensembles: combine different feature selectors (e.g., ANOVA, MI, Chi²).

• Unified aggregator API: aggregate results from scores, ranks, or binary supports.

• Visualization tools: plot selection frequency, consensus ranks, and stability matrices.

• Stability metrics: compute indices such as Kuncheva, Jaccard, or Spearman correlation.

• Extensible design: register custom selectors and aggregators via a single factory call.

Get started

Example using a built-in dataset and a simple configuration:

import pyensemblefs
from pyensemblefs.datasets import load_pima_dataset

# Load dataset
df = load_pima_dataset()

# Retrieve a pre-defined configuration (e.g., Relief filter)
cfg = pyensemblefs.get_config('relief', n_bootstrap=100, agg='voting')

# Compute feature scores
df_feature_scores = pyensemblefs.compute_scores(cfg, df)

# Extract the most relevant features
top_features, df_filtered = pyensemblefs.extract_features(10, df_feature_scores)

Heterogeneous ensemble feature selection

from sklearn.datasets import load_breast_cancer
from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, chi2
from pyensemblefs.ensemble.bootstrapper import Bootstrapper

X, y = load_breast_cancer(return_X_y=True)

fs_methods = [
    SelectKBest(score_func=f_classif, k=10),
    SelectKBest(score_func=mutual_info_classif, k=10),
    SelectKBest(score_func=chi2, k=10),
]

# Assign higher weight to ANOVA
weights = {"SelectKBest": 2.0}

boot = MetaBootstrapper(
    fs_methods,
    n_bootstraps=20,
    n_jobs=4,
    random_state=42,
    strategy="random",
    normalize_scores=True,
    method_weights=weights,
    verbose=True
)

boot.fit(X, y)

print("First bootstrap method:", boot.methods_used_[0])
print("First bootstrap binary support:", boot.results_[0])
if boot.score_mat_ is not None:
    print("Normalized + weighted scores:", boot.score_mat_[0][:10])

Visualization of frequency, top-k, and stability

from sklearn.datasets import load_breast_cancer
from sklearn.feature_selection import SelectKBest, mutual_info_classif
from pyensemblefs.ensemble.bootstrapper import Bootstrapper
from pyensemblefs.aggregators.rank import MeanRankAggregator
from pyensemblefs.aggregators.score import MeanAggregator
from pyensemblefs.viz.visualizer import Visualizer

X, y = load_breast_cancer(return_X_y=True)
fs = SelectKBest(score_func=mutual_info_classif, k=10)
boot = Bootstrapper(fsmethod=fs, n_bootstraps=25, n_jobs=2)
boot.fit(X, y)

# Aggregators
rank_agg = MeanRankAggregator(top_k=10).fit(boot.results_)
mean_agg = MeanAggregator(top_k=10).fit(boot.results_)

# Visualizations
Visualizer.stability_heatmap(boot.results_, n_features=X.shape[1])
Visualizer.compare_aggregators_heatmap({
    "RankAggregator": rank_agg.final_ranking_,
    "MeanAggregator": mean_agg.final_ranking_,
}, top_k=10)

All figures are automatically saved under ./images/.

Stability Metrics

Stability analysis quantifies how consistent the selected features remain across bootstrap samples.

from pyensemblefs.stability.evaluator import StabilityEvaluator

stab = StabilityEvaluator(metrics="jaccard")
stability_result = stab.compute(boot.results_)
print("Stability (Jaccard):", stability_result.summary)

import numpy as np
from pyensemblefs.stability.helpers import build_similarity
from pyensemblefs.stability.evaluator import StabilityEvaluator

sim_abs = build_similarity(X_tr_musk, mode="abs-corr")

eval12 = StabilityEvaluator(
    metrics="all12",
    mode="subset",
    sim_matrix=sim_abs,
    penalty=1.0
)

res_homo = eval12.compute(R_bin)
res_hetero = eval12.compute(R_bin_hetero)

print("Summary (homogeneous):", res_homo.summary)
print("Summary (heterogeneous):", res_hetero.summary)
print("\nPer-metric (homogeneous):", res_homo.values)
print("Per-metric (heterogeneous):", res_hetero.values)

Available metrics include Jaccard, Dice, Ochiai, Hamming, Novovicova, Davis, Lustgarten, Phi, Kappa, Nogueira, Yu, and Zucknick. They can be directly compared between homogeneous and heterogeneous ensembles.

Usage examples (scikit-learn compatible)

from sklearn.datasets import load_breast_cancer
from sklearn.feature_selection import SelectKBest, f_classif
from pyensemblefs.ensemble.bootstrapper import Bootstrapper
from pyensemblefs.aggregators.score import MeanAggregator
from pyensemblefs.aggregators.rank import MeanRankAggregator

X, y = load_breast_cancer(return_X_y=True)

fs = SelectKBest(score_func=f_classif, k=10)
boot = Bootstrapper(fs, n_bootstraps=30, n_jobs=4, random_state=42)
boot.fit(X, y)

# Aggregate scores and ranks
mean_agg = MeanAggregator().fit(boot.results_)
rank_agg = MeanRankAggregator().fit(boot.results_)

print("Consensus scores:", mean_agg.scores_[:10])
print("Consensus ranks:", rank_agg.rank_[:10])

from sklearn.feature_selection import SelectKBest, chi2
from pyensemblefs.ensemble.bootstrapper import Bootstrapper
from pyensemblefs.aggregators.score import MeanAggregator

fs = SelectKBest(score_func=chi2, k=5)
boot = Bootstrapper(fs, n_bootstraps=15, n_jobs=2)
boot.fit(X, y)

mean_agg = MeanAggregator().fit(boot.results_)
print("Consensus Scores (Chi2):", mean_agg.scores_)

How It Fits Together

Data → Bootstrapper / MetaBootstrapper
         ↓
Aggregators (Score / Rank / Subset)
         ↓
Visualizer / StabilityEvaluator → Reports

Citation

If you use pyensemblefs in academic work, please cite:

@software{pyensemblefs2025,
author = {Peralta-Arboleda B.A, Chushig-Muzo, C.D, Milara E, Conceição Granja, Soguero-Ruiz C.},
title = {pyensemblefs: Ensemble Feature Selection Library},
year = {2025},
url = {https://github.com/cdchushig/pyensemblefs} }

---measures

License

This project is licensed under the MIT License – see the LICENSE file for details.