GA algorithms and Logistic Regression

ga_logreg.py

from sklearn.metrics import *
mcc = make_scorer(matthews_corrcoef)
estimator = LogisticRegression(solver = "liblinear", C = 6, tol = 1, fit_intercept = True)

from sklearn.model_selection import *
report = pd.DataFrame()
nofeats = [] 
chosen_feats = [] 
cvscore = [] 
rkf = RepeatedStratifiedKFold(n_repeats = 2, n_splits = 10)
for i in range(2,11):
  
  selector = GeneticSelectionCV(estimator,
                                cv = rkf,
                                verbose = 0,
                                scoring = mcc,
                                max_features = i,
                                n_population = 200,
                                crossover_proba = 0.5,
                                mutation_proba = 0.2,
                                n_generations = 10,
                                crossover_independent_proba=0.5,
                                mutation_independent_proba=0.05,
                                #tournament_size = 3,
                                n_gen_no_change=10,
                                caching=True,
                                n_jobs=-1)
  selector = selector.fit(D[allfeats], y)
  genfeats = D[allfeats].columns[selector.support_]
  genfeats = list(genfeats)
  print("Chosen Feats:  ", genfeats)

  cv_score = selector.generation_scores_[-1]
  nofeats.append(len(genfeats)) 
  chosen_feats.append(genfeats) 
  cvscore.append(cv_score)
report["No of Feats"] = nofeats
report["Chosen Feats"] = chosen_feats
report["Scores"] = cvscore