def load_partial_annotations(self, filename, annotations_id):
if filename is None:
return
filename = path.join(self.dataset_conf.input_dir(self.secuml_conf),
'annotations', filename)
if not path.isfile(filename):
raise SecuMLexpException('The annotations file %s does not exist.'
% filename)
conn = self.session.connection().connection
cursor = conn.cursor()
call_specific_db_func(self.secuml_conf.db_type,
'load_partial_annotations',
(cursor, filename, annotations_id,
self.dataset_conf.dataset_id))
self.session.flush()
def getAlerts(exp_id, analysis_type):
exp = update_curr_exp(exp_id)
# With proba ? With scores ?
query = session.query(DiademExpAlchemy)
query = query.filter(DiademExpAlchemy.exp_id == exp_id)
diadem_exp = query.one()
with_proba, with_scores = diadem_exp.proba, diadem_exp.with_scoring
# Get alerts
query = session.query(PredictionsAlchemy)
query = query.filter(PredictionsAlchemy.exp_id == exp_id)
if with_proba:
threshold = exp.exp_conf.core_conf.detection_threshold
query = query.filter(PredictionsAlchemy.proba >= threshold)
else:
query = query.filter(
PredictionsAlchemy.value == label_str_to_bool(MALICIOUS))
if analysis_type == 'topN' and (with_proba or with_scores):
if with_proba:
query = query.order_by(PredictionsAlchemy.proba.desc())
else:
query = query.order_by(PredictionsAlchemy.score.desc())
elif analysis_type == 'random':
query = call_specific_db_func(secuml_conf.db_type, 'random_order',
(query, ))
query = query.limit(NUM_MAX_DISPLAY)
return jsonify(_predictions_results(query))
def load(self):
filepath, _ = self.get_filepath_hash()
has_ground_truth = call_specific_db_func(self.secuml_conf.db_type,
'load_idents',
(self.cursor, filepath,
self.dataset_conf.dataset_id)
)
self.secuml_conf.logger.info('Idents file for the dataset %s/%s '
'loaded into the database (%s).'
% (self.dataset_conf.project,
self.dataset_conf.dataset,
filepath))
return has_ground_truth
def getPredictionsScores(exp_id, range_, label):
score_min, score_max = [float(x) for x in range_.split(' - ')]
query = session.query(PredictionsAlchemy)
query = query.filter(PredictionsAlchemy.exp_id == exp_id)
query = query.filter(PredictionsAlchemy.score >= score_min)
query = query.filter(PredictionsAlchemy.score <= score_max)
query = query.order_by(PredictionsAlchemy.score.asc())
if label != 'all':
query = query.join(PredictionsAlchemy.instance)
query = query.filter(
InstancesAlchemy.label == label_str_to_bool(label))
query = call_specific_db_func(secuml_conf.db_type, 'random_order',
(query, ))
query = query.limit(NUM_MAX_DISPLAY)
return jsonify(_predictions_results(query))
def _get_errors(exp_id, fn_fp):
if fn_fp == 'FN':
predicted_value = BENIGN
ground_truth = label_str_to_bool(MALICIOUS)
else:
predicted_value = MALICIOUS
ground_truth = label_str_to_bool(BENIGN)
query = session.query(PredictionsAlchemy)
query = query.filter(PredictionsAlchemy.exp_id == exp_id)
query = query.filter(PredictionsAlchemy.value == predicted_value)
query = query.join(PredictionsAlchemy.instance)
query = query.filter(InstancesAlchemy.label == ground_truth)
query = call_specific_db_func(secuml_conf.db_type, 'random_order',
(query, ))
query = query.limit(NUM_MAX_DISPLAY)
return _predictions_results(query)
def getPredictionsProbas(exp_id, index, label):
index = int(index)
proba_min = index * 0.1
proba_max = (index + 1) * 0.1
query = session.query(PredictionsAlchemy)
query = query.filter(PredictionsAlchemy.exp_id == exp_id)
query = query.filter(PredictionsAlchemy.proba >= proba_min)
query = query.filter(PredictionsAlchemy.proba <= proba_max)
query = query.order_by(PredictionsAlchemy.proba.asc())
if label != 'all':
query = query.join(PredictionsAlchemy.instance)
query = query.filter(
InstancesAlchemy.label == label_str_to_bool(label))
query = call_specific_db_func(secuml_conf.db_type, 'random_order',
(query, ))
query = query.limit(NUM_MAX_DISPLAY)
return jsonify(_predictions_results(query))
def getPredictions(exp_id, predicted_value, right_wrong, multiclass):
multiclass = multiclass == 'true'
query = session.query(PredictionsAlchemy)
query = query.filter(PredictionsAlchemy.exp_id == exp_id)
query = query.filter(PredictionsAlchemy.value == predicted_value)
if right_wrong != 'all':
query = query.join(PredictionsAlchemy.instance)
if multiclass:
field = 'family'
else:
field = 'label'
predicted_value = label_str_to_bool(predicted_value)
if right_wrong == 'right':
query = query.filter(
getattr(InstancesAlchemy, field) == predicted_value)
elif right_wrong == 'wrong':
query = query.filter(
getattr(InstancesAlchemy, field) != predicted_value)
else:
assert (False)
query = call_specific_db_func(secuml_conf.db_type, 'random_order',
(query, ))
query = query.limit(NUM_MAX_DISPLAY)
return jsonify(_predictions_results(query))
def get_engine(self):
return call_specific_db_func(self.db_type, 'get_engine',
(self.db_uri, ))