def handle_report_metric_data(self, data):
"""
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported
"""
if data['type'] == MetricType.REQUEST_PARAMETER:
assert multi_phase_enabled()
assert data['trial_job_id'] is not None
assert data['parameter_index'] is not None
assert data['trial_job_id'] in self.job_id_para_id_map
self._handle_trial_end(
self.job_id_para_id_map[data['trial_job_id']])
ret = self._get_one_trial_job()
if data['trial_job_id'] is not None:
ret['trial_job_id'] = data['trial_job_id']
if data['parameter_index'] is not None:
ret['parameter_index'] = data['parameter_index']
self.job_id_para_id_map[data['trial_job_id']] = ret['parameter_id']
send(CommandType.SendTrialJobParameter, json_tricks.dumps(ret))
else:
value = extract_scalar_reward(data['value'])
bracket_id, i, _ = data['parameter_id'].split('_')
bracket_id = int(bracket_id)
# add <trial_job_id, parameter_id> to self.job_id_para_id_map here,
# because when the first parameter_id is created, trial_job_id is not known yet.
if data['trial_job_id'] in self.job_id_para_id_map:
assert self.job_id_para_id_map[
data['trial_job_id']] == data['parameter_id']
else:
self.job_id_para_id_map[
data['trial_job_id']] = data['parameter_id']
if data['type'] == MetricType.FINAL:
# sys.maxsize indicates this value is from FINAL metric data, because data['sequence'] from FINAL metric
# and PERIODICAL metric are independent, thus, not comparable.
self.brackets[bracket_id].set_config_perf(
int(i), data['parameter_id'], sys.maxsize, value)
self.completed_hyper_configs.append(data)
elif data['type'] == MetricType.PERIODICAL:
self.brackets[bracket_id].set_config_perf(
int(i), data['parameter_id'], data['sequence'], value)
else:
raise ValueError('Data type not supported: {}'.format(
data['type']))
def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported
"""
logger.debug('handle report metric data = %s', data)
if data['type'] == MetricType.REQUEST_PARAMETER:
assert multi_phase_enabled()
assert data['trial_job_id'] is not None
assert data['parameter_index'] is not None
assert data['trial_job_id'] in self.job_id_para_id_map
self._handle_trial_end(
self.job_id_para_id_map[data['trial_job_id']])
ret = self._get_one_trial_job()
if ret is None:
self.unsatisfied_jobs.append({
'trial_job_id':
data['trial_job_id'],
'parameter_index':
data['parameter_index']
})
else:
ret['trial_job_id'] = data['trial_job_id']
ret['parameter_index'] = data['parameter_index']
# update parameter_id in self.job_id_para_id_map
self.job_id_para_id_map[
data['trial_job_id']] = ret['parameter_id']
send(CommandType.SendTrialJobParameter, json_tricks.dumps(ret))
else:
assert 'value' in data
value = extract_scalar_reward(data['value'])
if self.optimize_mode is OptimizeMode.Maximize:
reward = -value
else:
reward = value
assert 'parameter_id' in data
s, i, _ = data['parameter_id'].split('_')
logger.debug('bracket id = %s, metrics value = %s, type = %s', s,
value, data['type'])
s = int(s)
# add <trial_job_id, parameter_id> to self.job_id_para_id_map here,
# because when the first parameter_id is created, trial_job_id is not known yet.
if data['trial_job_id'] in self.job_id_para_id_map:
assert self.job_id_para_id_map[
data['trial_job_id']] == data['parameter_id']
else:
self.job_id_para_id_map[
data['trial_job_id']] = data['parameter_id']
assert 'type' in data
if data['type'] == MetricType.FINAL:
# and PERIODICAL metric are independent, thus, not comparable.
assert 'sequence' in data
self.brackets[s].set_config_perf(int(i), data['parameter_id'],
sys.maxsize, value)
self.completed_hyper_configs.append(data)
_parameters = self.parameters[data['parameter_id']]
_parameters.pop(_KEY)
# update BO with loss, max_s budget, hyperparameters
self.cg.new_result(loss=reward,
budget=data['sequence'],
parameters=_parameters,
update_model=True)
elif data['type'] == MetricType.PERIODICAL:
self.brackets[s].set_config_perf(int(i), data['parameter_id'],
data['sequence'], value)
else:
raise ValueError('Data type not supported: {}'.format(
data['type']))