def anomalies_processing(datasets,
anomalies,
anomaly_ids,
api,
args,
resume,
fields=None,
session_file=None,
path=None,
log=None):
"""Creates or retrieves anomalies from the command data
"""
# If we have a dataset but not a model, we create the model if the no_model
# flag hasn't been set up.
if datasets and not (has_anomalies(args) or args.no_anomaly):
anomaly_ids = []
anomalies = []
# Only 1 anomaly detector per bigmler command at present
number_of_anomalies = 1
if resume:
resume, anomaly_ids = c.checkpoint(c.are_anomalies_created,
path,
number_of_anomalies,
debug=args.debug)
if not resume:
message = u.dated("Found %s anomaly detectors out of %s."
" Resuming.\n" %
(len(anomaly_ids), number_of_anomalies))
u.log_message(message,
log_file=session_file,
console=args.verbosity)
anomalies = anomaly_ids
number_of_anomalies -= len(anomaly_ids)
anomaly_args = r.set_anomaly_args(args,
fields=fields,
anomaly_fields=args.anomaly_fields_)
anomalies, anomaly_ids = r.create_anomalies(datasets, anomalies,
anomaly_args, args, api,
path, session_file, log)
# If an anomaly detector is provided, we use it.
elif args.anomaly:
anomaly_ids = [args.anomaly]
anomalies = anomaly_ids[:]
elif args.anomalies or args.anomaly_tag:
anomalies = anomaly_ids[:]
# If we are going to predict we must retrieve the anomalies
if anomaly_ids and args.test_set:
anomalies, anomaly_ids = r.get_anomalies(anomalies, args, api,
session_file)
return anomalies, anomaly_ids, resume
def anomalies_processing(datasets, anomalies, anomaly_ids,
api, args, resume, fields=None,
session_file=None, path=None,
log=None):
"""Creates or retrieves anomalies from the command data
"""
# If we have a dataset but not a model, we create the model if the no_model
# flag hasn't been set up.
if datasets and not (has_anomalies(args) or args.no_anomaly):
anomaly_ids = []
anomalies = []
# Only 1 anomaly detector per bigmler command at present
number_of_anomalies = 1
if resume:
resume, anomaly_ids = c.checkpoint(
c.are_anomalies_created, path, number_of_anomalies,
debug=args.debug)
if not resume:
message = u.dated("Found %s anomaly detectors out of %s."
" Resuming.\n"
% (len(anomaly_ids),
number_of_anomalies))
u.log_message(message, log_file=session_file,
console=args.verbosity)
anomalies = anomaly_ids
number_of_anomalies -= len(anomaly_ids)
anomaly_args = r.set_anomaly_args(args,
fields=fields,
anomaly_fields=args.anomaly_fields_)
anomalies, anomaly_ids = r.create_anomalies(datasets, anomalies,
anomaly_args, args, api,
path, session_file, log)
# If an anomaly detector is provided, we use it.
elif args.anomaly:
anomaly_ids = [args.anomaly]
anomalies = anomaly_ids[:]
elif args.anomalies or args.anomaly_tag:
anomalies = anomaly_ids[:]
# If we are going to predict we must retrieve the anomalies
if anomaly_ids and args.test_set:
anomalies, anomaly_ids = r.get_anomalies(anomalies, args, api,
session_file)
return anomalies, anomaly_ids, resume