def __run__(self) -> None:
if self.__runing:
print("Error! Process already started it\'s execution.")
return None
self.__runing = True
arg = p(target=stuff("python " + self.__task.get_path()), daemon=True)
arg.start()
del arg
return None
def main(maxIter, delta, stdInit):
from multiprocessing import Process as p
filename = "data/data"
numFeatures, numActions, k, thetaB, D = read_data(filename)
numCandidates = 20
process = []
for count in range(numCandidates):
process.append(
p(target=get_multiple_candidates,
args=(maxIter, delta, stdInit, numFeatures, numActions, k,
thetaB, D, count + 1)))
process[-1].start()
for proc in process:
proc.join()
return {k: x.get(k, 0) + y.get(k, 0) for k in set(x) | set(y)}
def average_dict(dict_a,iterations):
return {k: float(dict_a.get(k, 0))/iterations for k in set(dict_a)}
if __name__ == "__main__":
data_set_location = "/lv_local/home/sgregory/letor_fixed1"
q = qtf.qtf(data_set_location)
q.create_query_to_fold_index()
l = lfc.letor_folds_creator_z_normalize(data_set_location, data_set_location, True)
c = cv.cross_validator(5, l, "LTOR_MART_min_max")
lbda_score_file = "/lv_local/home/sgregory/LTOR_MART_min_max/test_scores_trec_format/LAMBDAMART/final_score_combined.txt"
svm_score_file = "/lv_local/home/sgregory/LTOR1/test_scores_trec_format/SVM/final_score_combined.txt"
rel_index = ri.relevance_index("qrels")
rel_index.create_relevance_index()
pool = p(3)
gg = d.lambda_mart_stats_handler("01", 0.1,c)
aa = d.lambda_mart_stats_handler("005", 0.05,c)
bb = d.lambda_mart_stats_handler("001", 0.01, c)
svm_gg = srfh.winner_reference_point_random("01",0.1)
svm_aa = srfh.winner_reference_point_random("005", 0.05)
svm_bb = srfh.winner_reference_point_random("001", 0.01)
lbda_chosen_models = gg.recover_models_per_fold("/lv_local/home/sgregory/LTOR_MART_min_max/models/LAMBDAMART",
"/lv_local/home/sgregory/LTOR_MART_min_max/test_scores_trec_format/LAMBDAMART/")
svm_chosen_models = svm_gg.recover_models_per_fold("/lv_local/home/sgregory/LTOR1/models/SVM",
"/lv_local/home/sgregory/LTOR1/test_scores_trec_format/SVM/")
lbda_f = partial(lbda_simulation, lbda_chosen_models, data_set_location, q.query_to_fold_index, lbda_score_file, "/lv_local/home/sgregory/LTOR_MART_min_max/competition", "/lv_local/home/sgregory/LTOR_MART_min_max/new_scores/", "/lv_local/home/sgregory/LTOR_MART_min_max/models/LAMBDAMART/",rel_index.rel_index)
lbda_g_input = [gg, aa, bb]
print('\033[36m')
print('made by tricker')
print('\033[0m')
url = input('url :')
thread = int(input('thread(default 500) :'))
def dos():
pass
def att():
while True:
dos()
def req():
p = rg(url)
print(p)
def a():
while True:
req()
for i in range(thread):
t = p(target=a)
t.start()
processes = []
@classmethod
def f(cls, args):
hub = subprocess.Popen([args])
cls.processes.append(hub)
print(args, '->', hub)
@classmethod
def stop_process(cls):
for i in cls.processes:
os.killpg(os.getpid(i.pid))
if __name__ == '__main__':
os.chdir("E:\\grid")
# os.system('dir')
# hub = subprocess.Popen(['java', '-jar', 'selenium-server-standalone.jar', '-role', 'hub'])
process_one_hub = p(target=Proc.f, args=('hub.bat', ))
process_one_hub.start() ## netstat -ano
############
time.sleep(5)
process_two_server = p(target=Proc.f, args=('node1.bat', ))
process_two_server.start()
Proc.stop_process()
10, data_set_location, 0.1,
chosen_models,
query_to_fold_index)
return c.competition(budget_creator.model)
if __name__ == "__main__":
data_set_location = "C:/study/letor_fixed1"
q = qtf.qtf(data_set_location)
q.create_query_to_fold_index()
score_file = "C:/study/simulation_data/test_scores_trec_format/SVM/final_score_combined.txt"
cost_model = "log_0.1.jpg"
pool = p(2)
"""lg =lb.logarithmic_budget_cost_creator("log")#l.linear_budget_cost_creator(factor) #e.exponential_budget_cost_creator()## # #
e = e.exponential_budget_cost_creator("exp")
li = l.linear_budget_cost_creator("linear")"""
lg = d.winner_reference_point("005", 0.05)
gg = d.winner_reference_point("001", 0.01)
#fg = d.distance_budget_cost_creator("003", 0.03)
chosen_models = lg.recover_models_per_fold(
"C:/study/simulation_data/models/SVM",
"C:/study/simulation_data/test_scores_trec_format/SVM/")
f = partial(simulation, chosen_models, data_set_location,
q.query_to_fold_index, score_file)
g_input = [gg, lg]
results = pool.map(f, g_input)