RFE Utility Function

from sklearn.feature_selection import RFE

# Define dictionary to store our rankings
ranks = {}
# Create our function which stores the feature rankings to the ranks dictionary
def ranking(ranks, names, order=1):
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]
    ranks = map(lambda x: round(x,2), ranks)
    return dict(zip(names, ranks))
    
# Do FRE feature importance scoring - 
# stop the search when only the last feature is left
rfe = RFE(estimator, n_features_to_select=1, verbose =3 )
rfe.fit(X, y)
ranks["RFE_RF"] = ranking(list(map(float, rfe.ranking_)), colnames, order=-1)