def create_multiple(request):
"""No request validation"""
payload = json.loads(request.body.decode('utf-8'))
try:
split = Split.objects.get(pk=payload['split_id'])
except Split.DoesNotExist:
return Response({'error': 'not in database'},
status=status.HTTP_404_NOT_FOUND)
# detect either or not a predictive_model to update has been specified otherwise train a new one.
if 'incremental_train' in payload['config'] and payload['config'][
'incremental_train']['base_model'] is not None:
jobs = update(split, payload)
elif payload['type'] in [e.value for e in PredictiveModels]:
jobs = generate(split, payload)
elif payload['type'] == JobTypes.LABELLING.value:
jobs = generate_labelling(split, payload)
else:
return Response(
{'error': 'type not supported'.format(payload['type'])},
status=status.HTTP_422_UNPROCESSABLE_ENTITY)
for job in jobs:
django_rq.enqueue(tasks.prediction_task, job.id)
serializer = JobSerializer(jobs, many=True)
return Response(serializer.data, status=201)
def test_update(self):
job = create_test_job()
prediction_task(job.id)
job2 = duplicate_orm_row(job)
job.refresh_from_db()
job2.incremental_train = job
job2.type = JobTypes.UPDATE.value
job2.save()
initial_job = job2 #.to_dict()
generated_job = update(split=job.split,
payload={
'type': 'classification',
'split_id': 1,
'config': {
'clusterings': ['noCluster'],
'encodings': ['simpleIndex'],
'encoding': {
'padding': False,
'prefix_length': 1,
'generation_type': 'only',
'add_remaining_time': False,
'add_elapsed_time': False,
'add_executed_events': False,
'add_resources_used': False,
'add_new_traces': False,
'features': [],
},
'create_models': False,
'methods': ['randomForest'],
'kmeans': {},
'incremental_train': [job.id],
'hyperparameter_optimizer': {
'algorithm_type': 'tpe',
'max_evaluations': 10,
'performance_metric': 'rmse',
'type': 'none',
},
'labelling': {
'type': 'next_activity',
'attribute_name': '',
'threshold_type': 'threshold_mean',
'threshold': 0,
}
}
})[0] #.to_dict()
def test_update(self):
job = create_test_job()
prediction_task(job.id)
# job2 = duplicate_orm_row(job) #todo: replace with simple CREATE
job2 = Job.objects.create(
created_date=job.created_date,
modified_date=job.modified_date,
error=job.error,
status=job.status,
type=job.type,
create_models=job.create_models,
case_id=job.case_id,
event_number=job.event_number,
gold_value=job.gold_value,
results=job.results,
parent_job=job.parent_job,
split=job.split,
encoding=job.encoding,
labelling=job.labelling,
clustering=job.clustering,
predictive_model=job.predictive_model,
evaluation=job.evaluation,
hyperparameter_optimizer=job.hyperparameter_optimizer,
incremental_train=job.incremental_train)
job.refresh_from_db()
job2.incremental_train = job
job2.type = JobTypes.UPDATE.value
job2.save()
initial_job = job2 #.to_dict()
generated_job = update(split=job.split,
payload={
'type': 'classification',
'split_id': 1,
'config': {
'clusterings': ['noCluster'],
'encodings': ['simpleIndex'],
'encoding': {
'padding': False,
'prefix_length': 1,
'generation_type': 'only',
'add_remaining_time': False,
'add_elapsed_time': False,
'add_executed_events': False,
'add_resources_used': False,
'add_new_traces': False,
'features': [],
},
'create_models': False,
'methods': ['randomForest'],
'kmeans': {},
'incremental_train': [job.id],
'hyperparameter_optimizer': {
'algorithm_type': 'tpe',
'max_evaluations': 10,
'performance_metric': 'rmse',
'type': 'none',
},
'labelling': {
'type': 'next_activity',
'attribute_name': '',
'threshold_type': 'threshold_mean',
'threshold': 0,
}
}
})[0] #.to_dict()