feature_selection.py

from niaaml.preprocessing.feature_selection import SelectKBest
import os
from niaaml.data import CSVDataReader
from sklearn.feature_selection import chi2

"""
This example presents how to use an implemented feature selection algorithm and its methods individually. In this case, we use SelectKBest for demonstration, but
you can use any of the implemented feature selection algorithms in the same way.
"""

# prepare data reader using csv file
data_reader = CSVDataReader(
    src=os.path.dirname(os.path.abspath(__file__)) + "/example_files/dataset.csv",
    has_header=False,
    contains_classes=True,
)

# instantiate SelectKBest feature selection algorithms
fs = SelectKBest()

# set parameters of the object
fs.set_parameters(k=4, score_func=chi2)

# select best features according to the SelectKBest algorithm (returns boolean mask of the selected features - True if selected, False if not)
features_mask = fs.select_features(data_reader.get_x(), data_reader.get_y())

# print feature selection algorithm in a user-friendly form
print(fs.to_string())