AML_Feature_Selection.py

# Read in pre-processed training data
file = 'ScaledDataSet.csv'
data = pd.read_csv(file)

def feature_selection(data,test):

    # Import libraries
    import numpy as np
    import pandas as pd
    from sklearn.feature_selection import RFE
    from sklearn.linear_model import LogisticRegression

    # Define our features and our target classifier
    X = data.iloc[:,:-1]
    Y = data.iloc[:,-1]

    # First, remove all features with zero variance i.e. features with constant values. 
    X = X.loc[:,X.apply(pd.Series.nunique) != 1]

    # RFE with logistic regression
    model = LogisticRegression()

    # Try 10 features
    rfe_log = RFE(model, 10)
    fit_rfe_log = rfe_log.fit(X, Y)
    # Create list with names of features
    rfe_log_features = X.columns[fit_rfe_log.get_support()]

    # Print names of selected features
    for f in rfe_log_features:
        print(f)

    # Create csv file with only the relevant features
    cols_to_use = rfe_log_features.union(['class'])
    fs_output = data[cols_to_use]
    fs_output.to_csv(r'FeatureSelectionOutput.csv', index=False)
    
    # Create csv file with test data and relevant features
    fs_test = test[cols_to_use]
    fs_test.to_csv(r'FeatureSelectionTestOutput.csv', index=False)