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_generate_up_to(self):
job = create_test_job()
initial_job = job.to_dict()
del initial_job['created_date']
del initial_job['modified_date']
del initial_job['hyperparameter_optimizer']
del initial_job['encoding']['features']
del initial_job['encoding']['task_generation_type']
del initial_job['labelling']['attribute_name']
generated_job = generate(split=job.split,
payload={
'type': 'classification',
'split_id': 1,
'config': {
'clusterings': ['noCluster'],
'encodings': ['simpleIndex'],
'encoding': {
'padding': False,
'prefix_length': 2,
'generation_type': 'up_to',
'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': {
'base_model': None,
},
'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,
},
'classification.decisionTree': {},
'classification.knn': {},
'classification.randomForest': {},
'classification.adaptiveTree': {},
'classification.hoeffdingTree': {},
'classification.multinomialNB': {},
'classification.perceptron': {},
'classification.SGDClassifier': {},
'classification.xgboost': {},
'classification.nn': {},
'regression.lasso': {},
'regression.linear': {},
'regression.randomForest': {},
'regression.xgboost': {},
'regression.nn': {},
'time_series_prediction.rnn': {}
}
})[0].to_dict()
del generated_job['created_date']
del generated_job['modified_date']
del generated_job['hyperparameter_optimizer']
del generated_job['encoding']['features']
del generated_job['encoding']['task_generation_type']
del generated_job['labelling']['attribute_name']
self.assertDictEqual(initial_job, generated_job)