from utils.plotting.image_creator import Image_Creator
from utils.printing import print_results
from utils.smote import execute_smote
from utils.split_preprocess_data import SplitPreprocessData
datasets = [
"paysim", "paysim-custom", "ccfraud", "ieee", "nslkdd", "saperp-ek",
"saperp-vk", "mnist", "cifar10"
]
methods = ["all", "ocan", "ocan-ae", "ae", "rbm", "vae", "dae", "cnn"]
baselines = ["both", "usv", "sv", "none"]
parser = Parser(datasets, methods, baselines)
dataset_string, verbosity, seed, method, baseline, iteration_count, use_oversampling, cross_validation_count = \
parser.get_args()
# Set parameters
parameter_class = Parameters(dataset_string)
usv_train, sv_train, sv_train_fraud, test_benign, test_fraud = \
parameter_class.get_main_parameters(cross_validation_count)
x_ben, x_fraud, preprocess_class = \
LoadData(dataset_string, parameter_class.get_path(), seed, parameter_class, verbosity).get_data()
# Initialize collections for evaluation results
prec_coll = list()
reca_coll = list()
f1_coll = list()
acc_coll = list()
