import os from dataSet import DataSet arquivo = open("saidaaa.csv", 'w') arquivo.write("CHAMOU PARTITION") arquivo.close() dts = DataSet() print("Iniciando particionamento....") arquivo = open("saidaaa.csv", 'a') arquivo.write("Iniciando particionamento....A") arquivo.close() #pasta pra salvar #dts.setFilePath("../../Bases/MachineLearningCVE/teste/") dts.setFilePath("../../Bases/MachineLearningCVE/DoS_56att2/") #caminho e nome do arquivo #dts.setFileName("../../Bases/MachineLearningCVE/teste_ddos_BINARY.csv") dts.setFileName( "../../Bases/MachineLearningCVE/Friday-WorkingHours-Afternoon-DDos_BINARY_56att.pcap_ISCX.csv" ) print("chamando load") arquivo = open("saidaaa.csv", 'a') arquivo.write("chamando load...A") arquivo.close() dts.loadData(10)
from evaluate_module import EvaluateModule dts = DataSet() dts.setFilePath("bases/gstav_first_attempt/") dts.setFileName("testes/base_iris.csv") #dts.setFileName("SmallTrainingSet.csv") #dts.setFileName("winequality-red.csv") #dts.setFileName("NSL_KDD-master/20PercentTrainingSet.csv") #dts.setFileName("NSL_KDD-master/KDDTrain+binary_class.csv") #dts.setFileName("NSL_KDD-master/SmallTrainingSet.csv") #dts.setFileName("NSL_KDD-master/SmallTrainingSetFiveClass.csv") #dts.setFileName("../../KDDCUP99/kddcup10%.csv") #print("load data") dts.loadData(6) ''' #CONFIGURACAO DO KNN knn = KnnModule() knn.setKNeighbors(1) knn_classifier = KnnClassifier() knn_classifier.setKnn(knn) #CONFIGURACAO DA REDE NEURAL rna = RnaModule() rna.setNumberNeuronsImputLayer(41) rna.setActivationFunctionImputLayer("tanh") rna.setImputDimNeurons(41)
folds = [] for indexes in splitter.split(x, y): folds.append(pd.DataFrame(dataset.values[indexes[1], ], columns=names)) return folds def writeFoldToCsv(fold, foldIndex, destinationPath): fold.to_csv(destinationPath + "fold_" + str(foldIndex) + ".csv", index=False) dts = DataSet() dts.setFilePath("../cicids2017/10-folds/") dts.setFileName("../cicids2017/total_selectedFeatures.csv") dts.loadData() directory = os.path.dirname(dts.file_path) if not os.path.exists(directory): os.makedirs(directory) dataset = dts.dataframe_data_set classFeatureName = dataset.columns[len(dataset.columns) - 1] #removing all instances that have no class value dataset = dts.dataframe_data_set.dropna(subset=[classFeatureName]) dataset = binarizeDataset(dataset, classFeatureName) folds = splitDataset(dataset, classFeatureName, 10)