cbfs 0.1.0

Floating Feature Selection guided by Strangeness Quantification

Bidirectional Floating Feature Selection Guided by Uncertainty Quantification

Authors: Marcos López-De-Castro, José González-Gomariz, Alberto García-Galindo Farnoosh Abbas-Aghababazadeh, Kewei Ni, Benjamin Haibe-Kains, Ruben Armañanzas Arnedillo,

Contact: [e-mail]

Description: Conformal prediction–driven feature selection, with applications in immuno-oncology datasets. We propose a novel bidirectional floating algorithm for feature selection named Conformal Bidirectional Floating Search Algorithm (CBFS), in which the feature search is enhanced by information from the conformal prediction framework.

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Set Up

Prerequisites

Clone environment in pixi.toml with

pixi install

or install via:

pip install cbfs

Usage

from cbfs import ffs


data_path = "your_path.h5ad" # .csv are also supported
target_column =  "target-column-name"
run_id= 0 # equivalent to random seed (int)

ffs_instance = ffs.FloatingFeatureSelector(run_id=run_id, data_path=data_path, target_column=target_column)
experiment_result = ffs_instance.run_ffs(n_feat=10)


print("Experiment completed successfully!")

print("Selected features:", experiment_result)

coverage = ffs_instance.Empirical_coverage_
uncertainty = ffs_instance.Uncertainty_
certainty = ffs_instance.Certainty_

print(f"Empirical coverage: {coverage}")
print(f"Uncertainty: {uncertainty}")
print(f"Certainty: {certainty}")

all_results[run_id] = {"selected_features": experiment_result, "run_id": run_id, "empirical_coverage": coverage,
                    "uncertainty": uncertainty, "certainty": certainty}