ml algos and dataset

import codecs


dataset = ["CIC-IDS-2017_10pc"] #,"CIC-IDS-2018","CIC-IDS-2019","NSL-KDD 2009",]
#Feature_Optimization = ["No_Feature_Optimization",  "Feature_Optimization_1", "Feature_Optimization_2", "Feature_Optimization_3", "Feature_Optimization_4", "Feature_Optimization_5", "Feature_Optimization_6", "Feature_Optimization_7", "Feature_Optimization_8", "Feature_Optimization_9", "Feature_Optimization_10", "Feature_Optimization_11", "Feature_Optimization_12", "Feature_Optimization_13", "Feature_Optimization_14", "Feature_Optimization_15", "Feature_Optimization_16", "Feature_Optimization_17", "Feature_Optimization_18", ]
Feature_Optimization = ["BBA", "CS", "DE", "EO", "FA", "FPA", "GA", "GSA", "GWO", "HHO", "HS", "JA", "MA", "NO_OPT", "PSO", "RDA", "SCA", "SSA", "WOA"]
smote = ["No_Smote", "Smote_1", "Smote_2"] #, "Smote_3", "Smote_4", "Smote_5"] #, "Smote_6", "Smote_7", "Smote_8", "Smote_9", "Smote_10", "Smote_11", "Smote_12" ] # , "Smote_13", "Smote_14", "Smote_15", "Smote_16", "Smote_17", "Smote_18", "Smote_19", "Smote_20", "Smote_21", "Smote_22", "Smote_23", "Smote_24", "Smote_25", "Smote_26", "Smote_27", "Smote_28", "Smote_29", "Smote_30", "Smote_31", "Smote_32", "Smote_33", "Smote_34", "Smote_35", "Smote_36", "Smote_37", "Smote_38", "Smote_39", "Smote_40", "Smote_41", "Smote_42", "Smote_43", "Smote_44", "Smote_45", "Smote_46", "Smote_47", "Smote_48", "Smote_49", "Smote_50", "Smote_51", "Smote_52", "Smote_53", "Smote_54", "Smote_55", "Smote_56", "Smote_57", "Smote_58", "Smote_59", "Smote_60"]
#ml = ["ML_Algo_1", "ML_Algo_2", "ML_Algo_3", "ML_Algo_4", "ML_Algo_5", "ML_Algo_6", "ML_Algo_7", "ML_Algo_8", "ML_Algo_9", "ML_Algo_10", "ML_Algo_11", "ML_Algo_12", "ML_Algo_13", "ML_Algo_14", "ML_Algo_15", "ML_Algo_16", "ML_Algo_17", "ML_Algo_18", "ML_Algo_19", "ML_Algo_20", "ML_Algo_21", "ML_Algo_22", "ML_Algo_23", "ML_Algo_24", "ML_Algo_25", "ML_Algo_26", "ML_Algo_27", "ML_Algo_28", "ML_Algo_29", "ML_Algo_30", "ML_Algo_31", "ML_Algo_32", "ML_Algo_33", "ML_Algo_34", "ML_Algo_35", "ML_Algo_36", "ML_Algo_37", "ML_Algo_38", "ML_Algo_39", "ML_Algo_40", "ML_Algo_41", "ML_Algo_42", "ML_Algo_43", "ML_Algo_44", "ML_Algo_45", "ML_Algo_46", "ML_Algo_47", "ML_Algo_48", "ML_Algo_49", "ML_Algo_50" ] # , "ML_Algo_51", "ML_Algo_52", "ML_Algo_53", "ML_Algo_54", "ML_Algo_55", "ML_Algo_56", "ML_Algo_57", "ML_Algo_58", "ML_Algo_59", "ML_Algo_60", "ML_Algo_61", "ML_Algo_62", "ML_Algo_63", "ML_Algo_64", "ML_Algo_65", "ML_Algo_66", "ML_Algo_67", "ML_Algo_68"]
ml = ["Adboost", "Artificial_neural_network_ANN", "AutoEncoderAE", "Bayes_Net", "Classification_and_Regression_Trees_CART", "constructive_learning", "CostSensitiveBaggingClassifier", "CostSensitiveLogisticRegression", "decision_treeDT", "Deep_auto_encoders_DA", "Deep_Boltzmann_machines_DBMs", "Deep_brief_networks_DBNs", "Deep_neural_networks_DNNs", "domain_adversarial_neural_networks", "ELM", "Ensemble_methods", "Ensemble_of_DL_Networks_EDLNs", "ensemble_sparse", "extra_tree", "extreme_learning_machine", "Feed_forward_Neural_Networks", "Fuzzy_systems", "Gated_recurrent_unit_GRU", "gaussian_naive_bayes", "GBT", "GMM", "gradient_boost_decision_tree", "Gradient_Boosting", "GRU", "Light_Gradient_Boosting_Machine_LGBM", "Linear_Discriminant_Analysis_LDA", "logistic_regression_LR", "LSTM", "MLP", "MultiLayer_Perceptron", "naive_BayesNB", "Nearest_Centroid", "Passive_Aggressive_Classifier", "QDA", "Quadratic_Discriminant_Analysis", "Random_forestRF", "Reconstruction_neural_networks", "Recurrent_neural_networks_RNNs", "Restricted_Boltzmann_machines_RBMs", "RF", "Ridge_Classifier", "Shallow_Neural_Network_SNN", "Stacked_Auto_encoders_SAE", "stochastic_gradient_descent", "support_vector_machine_SVM", "XGBoost_Extreme_Gradient_Boosting"]
#



#For Dataset Files
count=0
for x in dataset:
    for y in Feature_Optimization:
        for z in smote:
            # print(x,",",y,",",z)
            # print(count, end=" ")
            print(x.strip()+"_"+y.strip()+"_"+z.strip()+".csv")

            count+=1
            # print(count)



exit()
#For Results
count=0
for x in dataset:
    for y in Feature_Optimization:
        for z in smote:
            for m in ml:
                # print(x,",",y,",",z)
                # print(count, end=" ")
                print(x.strip()+"_"+y.strip()+"_"+z.strip()+"_"+m.strip()+".csv")

                count+=1
                # print(count)