def main(benchmark_name, dataset_name, dimensions, method_name, num_runs,
run_start, num_iterations, eta, min_budget, max_budget, input_dir,
output_dir):
benchmark = make_benchmark(benchmark_name,
dimensions=dimensions,
dataset_name=dataset_name,
input_dir=input_dir)
name = make_name(benchmark_name,
dimensions=dimensions,
dataset_name=dataset_name)
output_path = Path(output_dir).joinpath(name, method_name)
output_path.mkdir(parents=True, exist_ok=True)
options = dict(eta=eta, min_budget=min_budget, max_budget=max_budget)
with output_path.joinpath("options.yaml").open('w') as f:
yaml.dump(options, f)
for run_id in range(run_start, num_runs):
NS = hpns.NameServer(run_id=run_id, host='localhost', port=0)
ns_host, ns_port = NS.start()
num_workers = 1
workers = []
for worker_id in range(num_workers):
w = BenchmarkWorker(benchmark=benchmark,
nameserver=ns_host,
nameserver_port=ns_port,
run_id=run_id,
id=worker_id)
w.run(background=True)
workers.append(w)
rs = RandomSearch(configspace=benchmark.get_config_space(),
run_id=run_id,
nameserver=ns_host,
nameserver_port=ns_port,
ping_interval=10,
**options)
results = rs.run(num_iterations, min_n_workers=num_workers)
rs.shutdown(shutdown_workers=True)
NS.shutdown()
data = HpBandSterLogs(results).to_frame()
data.to_csv(output_path.joinpath(f"{run_id:03d}.csv"))
return 0
def get_parameters(train_data, kFold, iterations, save=False, filepath = './result/loss_time_rs.csv'):
parser = argparse.ArgumentParser(description='Example 1 - sequential and local execution.')
parser.add_argument('--min_budget', type=float, help='Minimum budget used during the optimization.', default=1)
parser.add_argument('--max_budget', type=float, help='Maximum budget used during the optimization.', default=1)
parser.add_argument('--n_iterations', type=int, help='Number of iterations performed by the optimizer', default=iterations) # max value = 4
# parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true')
parser.add_argument('--shared_directory', type=str,help='A directory that is accessible for all processes, e.g. a NFS share.', default='./result')
# parser.add_argument('--nic_name', type=str, default='lo')
args = parser.parse_args()
result_logger = hpres.json_result_logger(directory=args.shared_directory, overwrite=True)
NS = hpns.NameServer(run_id='RandomSearch', host='127.0.0.1', port=None)
NS.start()
w = worker(train_data, kFold, nameserver='127.0.0.1', run_id='RandomSearch')
w.run(background=True)
randomSearch = RandomSearch(configspace=w.get_configspace(),
run_id='RandomSearch', nameserver='127.0.0.1',
eta=3,
min_budget=args.min_budget, max_budget=args.max_budget,
result_logger=result_logger
)
res = randomSearch.run(n_iterations=args.n_iterations)
randomSearch.shutdown(shutdown_workers=True)
NS.shutdown()
id2config = res.get_id2config_mapping()
incumbent = res.get_incumbent_id()
info = res.get_runs_by_id(incumbent)
parameter = id2config[incumbent]['config']
min_error = info[0]['loss']
if save:
all_info = res.get_all_runs()
timepoint_dic = []
loss_dic = []
for i in all_info:
timepoint_dic.append(i['time_stamps']['finished'])
loss_dic.append(i['loss'])
save_to_csv.save(filepath, timepoint_dic, loss_dic)
return parameter, min_error
w = MyWorker(nameserver='127.0.0.1', run_id='example1')
w.run(background=True)
# Step 3: Run an optimizer
# Now we can create an optimizer object and start the run.
# Here, we run RandomSearch, but that is not essential.
# The run method will return the `Result` that contains all runs performed.
rs = RandomSearch(configspace=w.get_configspace(),
run_id='example1', nameserver='127.0.0.1',
min_budget=int(args.budget), max_budget=int(args.budget))
res = rs.run(n_iterations=args.n_iterations)
# Step 4: Shutdown
# After the optimizer run, we must shutdown the master and the nameserver.
rs.shutdown(shutdown_workers=True)
NS.shutdown()
# Step 5: Analysis
# Each optimizer returns a hpbandster.core.result.Result object.
# It holds information about the optimization run like the incumbent (=best) configuration.
# For further details about the Result object, see its documentation.
# Here we simply print out the best config and some statistics about the performed runs.
id2config = res.get_id2config_mapping()
incumbent = res.get_incumbent_id()
print('Best found configuration:', id2config[incumbent]['config'])
# Plots the performance of the best found validation error over time
all_runs = res.get_all_runs()
nameserver_port=ns_port,
result_logger=result_logger,
min_budget=args.min_budget,
max_budget=args.max_budget,
previous_result=previous_run)
res = hpo_worker.run(n_iterations=args.n_iterations,
min_n_workers=args.n_workers)
# store results
if (args.default_config == False) and (args.test_mode == False):
with open(os.path.join(hpo_results_dir, inter_results_file),
'wb') as fh:
pickle.dump(res, fh)
hpo_worker.shutdown(shutdown_workers=True)
# In[14]:
# Step 4: Shutdown
# After the optimizer run, we must shutdown the master and the nameserver.
# In[12]:
NS.shutdown()
# In[13]:
# Evaluate
test_results = test_model(workdir, dataset, ranklib_location,