def Elastic_selection(k, X, y): elasticnet = ElasticNet() elasticresult = RFE(elasticnet, k).fit(X, y.values.ravel()).get_support(indices=True) elasticresult = X[X.columns[elasticresult]] elasticresult.to_csv("ElasticNet_out.csv") return None
def L1SVM_selection(k, X, y): lsvc = LinearSVC(penalty="l1", dual=False) lsvcresult = RFE(lsvc, k).fit(X, y.values.ravel()).get_support(indices=True) lsvcresult = X[X.columns[lsvcresult]] lsvcresult.to_csv("L1SVM_out.csv") return None
def Lasso_selection(k, X, y): clf = LassoCV(cv=k) lassoresult = RFE(clf, k).fit(X, y.values.ravel()).get_support(indices=True) lassoresult = X[X.columns[lassoresult]] lassoresult.to_csv("Lasso_out.csv") return None
def ExtraTree_selection(k, X, y): extraT = ExtraTreesClassifier(n_estimators=10) extraresult = RFE(extraT, k).fit(X, y.values.ravel()).get_support(indices=True) extraresult = X[X.columns[extraresult]] extraresult.to_csv("ExtraTree_out.csv") return None
def LOG_RFE_selection(k, X, y): log = LogisticRegression(solver='liblinear') logresult = RFE(log, k).fit(X, y.values.ravel()).get_support(indices=True) logresult = X[X.columns[logresult]] logresult.to_csv("LOG-FRE_out.csv") return None
def SVM_RFE_selection(k, X, y): svc = SVC(kernel="linear") svmresult = RFE(svc, k).fit(X, y.values.ravel()).get_support(indices=True) svmresult = X[X.columns[svmresult]] svmresult.to_csv("SVM-RFE_out.csv") return None
def XGB_selection(k, X, y): xgb = XGBClassifier() xgbresult = RFE(xgb, k).fit(X, y.values.ravel()).get_support(indices=True) xgbresult = X[X.columns[xgbresult]] xgbresult.to_csv("XGBoost_out.csv") return None