epsilons = [0.1, 0.2, 0.4, 1.0, 2.0]
#epsilons = [0.1, 0.2]
degree = 2
dimension = 5
data_size = "1M"
jar_name = "PrivBayes.jar"
source_file = "%sdata_%ddim_%s-coarse.csv" % (resource_path, dimension, data_size)
domain_file = "%sdata_%ddim_%s-coarse.domain.csv" % (resource_path, dimension, data_size)
for ep in epsilons:
test_prefix = "%dD%s%sE%dK" % (dimension, data_size, convert_epislon(ep), degree)
arguments = " %s %s %f %d %s" % (source_file, domain_file, ep, degree, convert_epislon(ep))
command = "java -Xmx10g -jar %s%s %s" % (jar_path, jar_name, arguments)
result_file = "%s%s_SyntheticData.csv" % (result_path, test_prefix)
acc_check = Summary(dimension, data_size, ep, source_file, result_file)
for round in range(test_round):
# run the java command
print "Command: %s, Round %d" % (command, round+1)
run_command(command)
# after privbayes, do the accuracy check
acc_check.statistic_accuracy()
acc_check.query_accuracy()
acc_check.print_summaries("/privbayes_test/test_round_1")
