def checkInputParams(self, experiment):
if self.auto:
# Check true labels are available for the oracle
if not db_tables.hasTrueLabels(experiment):
raise InvalidInputArguments(
'true_labels.csv must be provided to run Active Learning with an oracle.'
)
def getLabels(self, num_instances):
labels = [None] * num_instances
families = [None] * num_instances
annotations = [False] * num_instances
true_labels = [None] * num_instances
true_families = [None] * num_instances
## Labels/Families
benign_ids = labels_tools.getLabelIds(self.experiment, 'benign')
malicious_ids = labels_tools.getLabelIds(self.experiment, 'malicious')
for instance_id in benign_ids + malicious_ids:
label, family, method, annotation = labels_tools.getLabelDetails(
self.experiment, instance_id)
labels[self.indexes[instance_id]] = label == 'malicious'
families[self.indexes[instance_id]] = family
annotations[self.indexes[instance_id]] = annotation
## True Labels
has_true_labels = db_tables.hasTrueLabels(self.experiment)
if has_true_labels:
true_labels, true_families = labels_tools.getTrueLabelsFamilies(
self.experiment)
return labels, families, annotations, true_labels, true_families
def getConf(experiment_id):
experiment = updateCurrentExperiment(experiment_id)
conf = experiment.toJson()
conf['has_true_labels'] = db_tables.hasTrueLabels(experiment)
return jsonify(conf)
def getTrueLabelsExperiment(experiment_id):
experiment = updateCurrentExperiment(experiment_id)
true_labels_exp_id = db_tables.hasTrueLabels(experiment)
return str(true_labels_exp_id)