def replay_task(replay_job: Job, training_initial_job: Job) -> list:
""" The function create a replay task to ask the server to demo the arriving of events
:param replay_job: job dictionary
:param training_initial_job: job dictionary
:return: List of requests
"""
logger.error("Start replay task ID {}".format(replay_job.id))
requests = list()
try:
replay_job.status = JobStatuses.RUNNING.value
replay_job.error = ''
replay_job.save()
requests = replay_core(replay_job, training_initial_job)
replay_job.status = JobStatuses.COMPLETED.value
for r in requests:
if r.status_code != status.HTTP_201_CREATED:
replay_job.error += [r]
except Exception as e:
logger.error(e)
replay_job.status = JobStatuses.ERROR.value
replay_job.error += [str(e.__repr__())]
raise e
finally:
replay_job.save()
publish(replay_job)
return requests
def runtime_task(job: Job):
""" The function create a runtime task to ask a single prediction to the server
:param job: job dictionary
"""
logger.info("Start runtime task ID {}".format(job.id))
try:
job.status = JobStatuses.RUNNING.value
job.save()
result = runtime_calculate(job)
job.results = {'result': str(result)}
job.status = JobStatuses.COMPLETED.value
job.error = ''
except Exception as e:
logger.error(e)
job.status = JobStatuses.ERROR.value
job.error = str(e.__repr__())
raise e
finally:
job.save()
publish(job)
def replay_prediction_task(replay_prediction_job: Job, training_initial_job: Job, log: Log):
""" The function create a replat prediction task to ask a single prediction to the server for a portion of a trace
:param replay_prediction_job: job dictionary
:param training_initial_job: job dictionary
:param log: job dictionary
"""
logger.info("Start replay_prediction task ID {}".format(replay_prediction_job.id))
try:
replay_prediction_job.status = JobStatuses.RUNNING.value
replay_prediction_job.save()
max_len = max(len(trace) for trace in log)
if replay_prediction_job.encoding.prefix_length != max_len:
prediction_job = create_prediction_job(training_initial_job, max_len)
prediction_task(prediction_job.id)
prediction_job.refresh_from_db()
new_replay_prediction_job = duplicate_orm_row(prediction_job)
new_replay_prediction_job.split = Split.objects.filter(pk=replay_prediction_job.split.id)[0]
new_replay_prediction_job.type = JobTypes.REPLAY_PREDICT.value
new_replay_prediction_job.parent_job = replay_prediction_job.parent_job
new_replay_prediction_job.status = JobStatuses.CREATED.value
replay_prediction_task(new_replay_prediction_job, prediction_job, log)
return
result_dict, events_for_trace = replay_prediction_calculate(replay_prediction_job, log)
replay_prediction_job.results = dict(result_dict)
replay_prediction_job.event_number = dict(events_for_trace)
replay_prediction_job.status = JobStatuses.COMPLETED.value
replay_prediction_job.error = ''
except Exception as e:
logger.error(e)
replay_prediction_job.status = JobStatuses.ERROR.value
replay_prediction_job.error = str(e.__repr__())
raise e
finally:
replay_prediction_job.save()
publish(replay_prediction_job)
def replay_prediction_task(replay_prediction_job: Job,
training_initial_job: Job, log: Log):
""" The function create a replat prediction task to ask a single prediction to the server for a portion of a trace
:param replay_prediction_job: job dictionary
:param training_initial_job: job dictionary
:param log: job dictionary
"""
logger.info("Start replay_prediction task ID {}".format(
replay_prediction_job.id))
try:
replay_prediction_job.status = JobStatuses.RUNNING.value
replay_prediction_job.save()
max_len = max(len(trace) for trace in log)
if replay_prediction_job.encoding.prefix_length != max_len:
prediction_job = create_prediction_job(training_initial_job,
max_len)
prediction_task(prediction_job.id)
prediction_job.refresh_from_db()
# new_replay_prediction_job = duplicate_orm_row(prediction_job) #todo: replace with simple CREATE
new_replay_prediction_job = Job.objects.create(
created_date=prediction_job.created_date,
modified_date=prediction_job.modified_date,
error=prediction_job.error,
status=prediction_job.status,
type=prediction_job.type,
create_models=prediction_job.create_models,
case_id=prediction_job.case_id,
event_number=prediction_job.event_number,
gold_value=prediction_job.gold_value,
results=prediction_job.results,
parent_job=prediction_job.parent_job,
split=prediction_job.split,
encoding=prediction_job.encoding,
labelling=prediction_job.labelling,
clustering=prediction_job.clustering,
predictive_model=prediction_job.predictive_model,
evaluation=prediction_job.evaluation,
hyperparameter_optimizer=prediction_job.
hyperparameter_optimizer,
incremental_train=prediction_job.incremental_train)
new_replay_prediction_job.split = Split.objects.filter(
pk=replay_prediction_job.split.id)[0]
new_replay_prediction_job.type = JobTypes.REPLAY_PREDICT.value
new_replay_prediction_job.parent_job = replay_prediction_job.parent_job
new_replay_prediction_job.status = JobStatuses.CREATED.value
replay_prediction_task(new_replay_prediction_job, prediction_job,
log)
return
result_dict, events_for_trace = replay_prediction_calculate(
replay_prediction_job, log)
replay_prediction_job.results = dict(result_dict)
replay_prediction_job.event_number = dict(events_for_trace)
replay_prediction_job.status = JobStatuses.COMPLETED.value
replay_prediction_job.error = ''
except Exception as e:
logger.error(e)
replay_prediction_job.status = JobStatuses.ERROR.value
replay_prediction_job.error = str(e.__repr__())
raise e
finally:
replay_prediction_job.save()
publish(replay_prediction_job)