def main():
dataset = 'adult'
workload = 8
marginal = 3
data, workloads = benchmarks.randomKway(dataset, workload, marginal)
N = data.df.shape[0]
######################################################
## Get Queries
######################################################
stime = time.time()
query_manager = QueryManager(data.domain, workloads)
print("Number of queries = ", len(query_manager.queries))
W_p = query_manager.get_query_workload([1, 4, 7, 100])
W_n = query_manager.get_query_workload([2, 5, 10,
11]) # q_neg(D) = 1 - q(D)
D = W_p.shape[1]
noise = np.random.exponential(1, D)
# noise = np.zeros( D)
print(f'noise.shape = {noise.shape}')
x = oracle_weighted.solve(W_p, np.ones(4), W_n, np.ones(4), noise,
data.domain, 0)
print(f'best score = {get_score(x, W_p, W_n, noise, marginal)}')
type=float,
default=0.05,
help='noise hyperparameter')
parser.add_argument('--samples',
type=int,
default=50,
help='samples hyperparameter')
args = parser.parse_args()
print("=============================================")
print(vars(args))
######################################################
## Get dataset
######################################################
data, workloads = benchmarks.randomKway(args.dataset[0], args.workload[0],
args.marginal[0])
N = data.df.shape[0]
delta = 1.0 / N**2
######################################################
## Get Queries
######################################################
stime = time.time()
query_manager = QueryManager(data.domain, workloads)
print("Number of queries = ", len(query_manager.queries))
real_ans = query_manager.get_answer(data)
res = []
for eps in args.epsilon: