Data/boxPlot2.py at main · abstract-machine-learning/Data · GitHub

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import exec

print("\n\n*** DO NOT EXECUTE TWO SCRIPTS IN PARALLEL ***\n\n")

print(" 1) Adult \n 2) Compas \n 3) Crime \n 4) German \n 5) Health")
choice = int(input("Enter choice: "))

if(choice == 1):
	data_folder = "adult"
	reg_params = [1,0.05,0.01] #for linear rbf and poly respectively
	gammas = [0.01,0.03,0.05,0.07,0.09]
	degrees = [3]
	coef0s =  list(range(0,16,3))[1:]
	abstractions = ['raf']
	perturbations = ["noisecat"]
	kernel_types = ['linear','rbf','poly']
	exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False, is_OH = 1, get_CE = 0, if_part = 0,if_print_raw=True, plot = 'boxplotDatasets')

if(choice == 2):
	data_folder = "compas"
	reg_params = [1,0.05,0.01]
	gammas = [0.01,0.03,0.05,0.07,0.09]
	degrees = [3]
	coef0s =  list(range(0,16,3))[1:]
	abstractions = ['raf']
	perturbations = ["noisecat"]
	kernel_types = ['linear','rbf','poly']
	exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False, is_OH = 1, get_CE = 0, if_part = 0,if_print_raw=True, plot = 'boxplotDatasets')

if(choice == 3):
	data_folder = "crime"
	reg_params = [1,1,1]
	gammas = [0.01,0.001,0.0001,0.00001]
	degrees = [3,9,15,20]
	coef0s = [0]
	abstractions = ['raf']
	perturbations = ["noisecat"]
	kernel_types = ['linear','rbf','poly']
	exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False, is_OH = 1, get_CE = 0, if_part = 0,if_print_raw=True, plot = 'boxplotDatasets')

if(choice == 4):
	data_folder = "german"
	reg_params = [1,10,0.01]#[1,10,0.01]
	gammas = [0.01,0.03,0.05,0.07,0.09]
	degrees = [6]
	coef0s =  list(range(0,16,3))[1:]
	abstractions = ['raf']
	perturbations = ["noisecat"]
	kernel_types = ['linear','rbf','poly']
	exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False, is_OH = 1, get_CE = 0, if_part = 0,if_print_raw=True, plot = 'boxplotDatasets')

if(choice == 5):
	data_folder = "health"
	reg_params = [0.01,0.1,0.1]
	gammas = [0.01]
	degrees = [3]
	coef0s =  [0.01]
	abstractions = ['raf']
	perturbations = ["noisecat"]
	kernel_types = ['linear','rbf','poly']
	exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False, is_OH = 1, get_CE = 0, if_part = 0,if_print_raw=True, plot = 'boxplotDatasets')


print(f"\n\n\n OUTPUT LOCATION: \n" +
	f"1) Check ./{data_folder}/{data_folder}-results.txt for detailed results\n" +
	f"2) Check ./{data_folder}/{data_folder}-results_raw.txt for summary where\n" +
	"the coloums are in the order Accuracy, Balanced Accuracy and Robutness\n")