E2E-FS

E2E-FS: An End-to-End Feature Selection Method for Neural Networks

CONTACT

This project is hosted at https://github.com/braisCB/E2E-FS.

REFERENCE

If you plan to use this code, please cite the following paper:

Cancela, B., Bolón-Canedo, V., & Alonso-Betanzos, A. (2020). E2E-FS: An End-to-End Feature Selection Method for Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. (Pending on publication)

EXAMPLE OF USE (for colon dataset)

from dataset_reader import colon
import numpy as np
import e2efs


n_features_to_select = 10

if __name__ == '__main__':

    ## LOAD DATA
    dataset = colon.load_dataset()
    raw_data = np.asarray(dataset['raw']['data'])
    raw_label = np.asarray(dataset['raw']['label']).reshape(-1)
    train_data = raw_data[:int(len(raw_data) * 0.8)]
    train_label = raw_label[:int(len(raw_label) * 0.8)]
    test_data = raw_data[int(len(raw_data) * 0.8):]
    test_label = raw_label[int(len(raw_label) * 0.8):]
    normalize = colon.Normalize()
    train_data = normalize.fit_transform(train_data)
    test_data = normalize.transform(test_data)

    ## LOAD E2EFSSoft model
    model = e2efs.E2EFSSoft(n_features_to_select=n_features_to_select)

    ## OPTIONAL: Load E2EFS Model
    # model = e2efs.E2EFS(n_features_to_select=n_features_to_select)

    ## OPTIONAL: Load E2EFSRanking Model
    # model = e2efs.E2EFSRanking()

    ## FIT THE SELECTION
    model.fit(train_data, train_label, validation_data=(test_data, test_label), batch_size=2, max_epochs=2000)

    ## FINETUNE THE MODEL
    model.fine_tune(train_data, train_label, validation_data=(test_data, test_label), batch_size=2, max_epochs=100)

    ## GET THE MODEL RESULTS
    metrics = model.evaluate(test_data, test_label)
    print(metrics)

    ## GET THE MASK
    mask = model.get_mask()
    print('MASK:', mask)

    ## GET THE RANKING
    ranking = model.get_ranking()
    print('RANKING:', ranking)