fuzzyforest 0.1.0

Fuzzy forest-style feature ranking using repeated random forests.

FuzzyForest

FuzzyForest is a small Python library that ranks features by stability using repeated random forests. It exposes:

• FuzzyForestSelector: a scikit-learn compatible transformer that averages feature importances across many resampled forests.
• FuzzyForestRegressor: a regressor that selects features with the selector, then fits a final random forest on the reduced feature set.

Installation

pip install fuzzyforest

If you are developing locally, install in editable mode from the repo root:

pip install -e .

Quickstart

import numpy as np
from sklearn.datasets import fetch_california_housing
from fuzzyforest import FuzzyForestSelector, FuzzyForestRegressor

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

# Rank and select the top 10 most stable features
selector = FuzzyForestSelector(top_k=10, n_resamples=30, random_state=42)
selector.fit(X, y)
print(selector.get_feature_ranking())        # ordered feature names
X_selected = selector.transform(X)           # reduced feature matrix

# End-to-end regression with built-in selection
model = FuzzyForestRegressor(top_k=10, random_state=42)
model.fit(X, y)
print(model.score(X, y))

How it works

The selector builds n_resamples random forests, each on a bootstrap of the rows and a random subset of the columns. It averages the resulting feature importances, promoting features that are consistently useful across many draws. You can control:

• sample_fraction and feature_fraction to change how aggressive the perturbations are.
• top_k and min_importance to decide which features to keep.
• task ("auto", "classification", "regression") to match the estimator.

Publishing

This project is configured for setuptools. Once you are ready to publish to GitHub or PyPI:

1. Update project.urls.Homepage in pyproject.toml to the real repository URL.
2. Build the distribution:

   python -m pip install build
   python -m build
   

3. Upload to PyPI (optional):

   python -m pip install twine
   twine upload dist/*