print template.render(title = key, kvps = data[key])
def compare(old,new):
mod = old
if len(old) is not len(new):
temp = new.keys()
for key in old.keys():
temp.pop(temp.index(key))
print("The following blocks are missing in your config.\n Please select an option for changing the configuratoin to the optimal value. [Y/n]")
print(temp)
dec = str(raw_input())
if __name__ == "__main__":
global data
parser = argparse.ArgumentParser(description = "Accepts addresses of config file")
parser.add_argument("--path1",dest = "path1")
parser.add_argument("--path2",dest = "path2")
paths = parser.parse_args()
if paths.path1 is None or paths.path2 is None:
print("Paths incomplete")
sys.exit()
if not os.path.exists(paths.path1) or not os.path.exists(paths.path2):
print("Files do not exist.")
sys.exit()
data1 = test_parser.parse(paths.path1)#"/home/akarsh/Documents/tmp/config2.conf")
data2 = test_parser.parse(paths.path2)#"/home/akarsh/Documents/git/NetApp-PESIT-Configuration-Validation-Tool/output/output.conf")
render_template(data1)
print data1 == data2
sys.exit(1)
else:
path = argv.pop(0)
debug_testno = [-1]
breakAt = -1
while argv:
arg = argv.pop(0)
if arg == '-b':
if argv: breakAt = int(argv.pop(0))
else: breakAt = 1
elif arg == '-l': debug_testno = argv[1:]
else: raise Exception('unknown flag')
server, tests = parse(path)
tlength = len(tests)
print
print 'Executing %d tests using %s' % (tlength, server)
completed = 0
for i, test in enumerate(tests):
i += 1
print '-'*80
debug = True if str(i) in debug_testno or '*' in debug_testno else False
if debug:
print
print("\n # Processing...")
parser(dataset_path)
print("\n # output.csv created\n")
print(" # Training...")
x = dataset_path + '/output.csv'
func(x)
print("\n # Train finished\n")
# python3 train.py ./data/consumer_complaints.csv.zip ./parameters.json
elif menu_choice == '2':
print(" ------------ ------------")
print(" Choose classification method (default SVC)\n")
print(" 1. Multinomial Naive Bayes")
print(" 2. Linear Support Vector Clusters")
print(" 3. Support Vector Clusters with ratio \n")
classification_choice = input(" Enter an input: ")
print(" Enter the path for folder (ex. format = ./dataset )")
document_path = input(" Enter an input: ")
parse(document_path)
print("# Classifying...")
predict('./test_output.csv', classification_choice)
elif menu_choice == '0':
break
else:
print("\n")