def collect_data(runs, X, y, opt_list, search_config, n_iter, opt_dict):
time_c = time.time()
data_runs_1 = []
data_runs_2 = []
for run in tqdm.tqdm(range(runs)):
print("\nRun nr.", run, "\n")
total_time_list = []
eval_time_list = []
for key in opt_list:
print("optimizer:", key)
n_iter_temp = n_iter
opt_dict_temp = opt_dict
if key == "ParallelTempering":
n_iter_temp = int(n_iter / 10)
if key == "ParticleSwarm":
n_iter_temp = int(n_iter / 10)
if key == "EvolutionStrategy":
n_iter_temp = int(n_iter / 10)
opt_obj = Cypher(search_config,
optimizer=key,
n_iter=n_iter_temp,
**opt_dict_temp)
opt_obj.search(X, y)
total_time = opt_obj.get_total_time()
eval_time = opt_obj.get_eval_time()
total_time_list.append(total_time)
eval_time_list.append(eval_time)
total_time_list = np.array(total_time_list)
eval_time_list = np.array(eval_time_list)
data_runs_1.append(total_time_list)
data_runs_2.append(eval_time_list)
data_runs_1 = np.array(data_runs_1)
data_runs_2 = np.array(data_runs_2)
print("\nCreate Dataframe\n")
print("data_runs_1", data_runs_1, data_runs_1.shape)
data = pd.DataFrame(data_runs_1, columns=opt_list)
model_name = list(search_config.keys())[0]
calc_optimizer_time_name = "total_time_" + model_name.__name__
file_name = str(calc_optimizer_time_name)
data.to_csv(file_name, index=False)
data = pd.DataFrame(data_runs_2, columns=opt_list)
calc_optimizer_time_name = "eval_time_" + model_name.__name__
file_name = str(calc_optimizer_time_name)
data.to_csv(file_name, index=False)
print("data collecting time:", time.time() - time_c)
def test_get_eval_time():
opt = Cypher(X, y)
opt.search(search_config, n_iter=n_iter, optimizer="HillClimbing")
opt.get_eval_time()