def _create_detection_exp(self, kind, classifier_conf, fold_id=None):
diadem_id = self.exp_conf.exp_id
exp_name = 'DIADEM_%i_Detection_%s' % (diadem_id, kind)
if fold_id is not None:
exp_name = '%s_fold_%i' % (exp_name, fold_id)
secuml_conf = self.exp_conf.secuml_conf
logger = secuml_conf.logger
if kind == 'validation':
dataset_conf = DatasetConf(self.exp_conf.dataset_conf.project,
self.validation_conf.test_dataset,
self.exp_conf.secuml_conf.logger)
annotations_conf = AnnotationsConf('ground_truth.csv', None,
logger)
elif kind == 'test' and self.test_conf.method == 'dataset':
dataset_conf = DatasetConf(self.exp_conf.dataset_conf.project,
self.test_conf.test_dataset,
self.exp_conf.secuml_conf.logger)
annotations_conf = AnnotationsConf('ground_truth.csv', None,
logger)
else:
dataset_conf = self.exp_conf.dataset_conf
annotations_conf = self.exp_conf.annotations_conf
features_conf = self.exp_conf.features_conf
test_exp_conf = DetectionConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
self._get_alerts_conf(fold_id),
name=exp_name,
parent=diadem_id,
fold_id=fold_id,
kind=kind)
return DetectionExp(test_exp_conf, session=self.session)
def _create_test_exp(self, fold_id=None):
diadem_id = self.exp_conf.exp_id
exp_name = 'DIADEM_%i_Test' % diadem_id
if fold_id is not None:
exp_name = '%s_fold_%i' % (exp_name, fold_id)
secuml_conf = self.exp_conf.secuml_conf
logger = secuml_conf.logger
if self.test_conf.method == 'dataset':
dataset_conf = DatasetConf(self.exp_conf.dataset_conf.project,
self.test_conf.test_dataset,
self.exp_conf.secuml_conf.logger)
annotations_conf = AnnotationsConf('ground_truth.csv', None,
logger)
else:
dataset_conf = self.exp_conf.dataset_conf
annotations_conf = self.exp_conf.annotations_conf
features_conf = self.exp_conf.features_conf
test_exp_conf = TestConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
self.exp_conf.core_conf.classifier_conf,
name=exp_name,
parent=diadem_id,
fold_id=fold_id,
kind='test')
return TestExp(test_exp_conf,
alerts_conf=self._get_alerts_conf(fold_id),
session=self.session)
def _create_detection_conf(self, kind, classifier_conf, fold_id=None):
diadem_id = self.exp_conf.exp_id
exp_name = 'DIADEM_%i_Detection_%s' % (diadem_id, kind)
if fold_id is not None:
exp_name = '%s_fold_%i' % (exp_name, fold_id)
secuml_conf = self.exp_conf.secuml_conf
logger = secuml_conf.logger
if (kind == 'validation'
or (kind == 'test' and self.test_conf.method == 'datasets')):
validation_conf = getattr(self, '%s_conf' % kind)
annotations_conf = AnnotationsConf('ground_truth.csv', None,
logger)
features_conf = self.exp_conf.features_conf
if validation_conf.streaming:
stream_batch = validation_conf.stream_batch
features_conf = features_conf.copy_streaming(stream_batch)
dataset_confs = [
DatasetConf(self.exp_conf.dataset_conf.project, test_dataset,
self.exp_conf.secuml_conf.logger)
for test_dataset in validation_conf.validation_datasets
]
else:
dataset_confs = [self.exp_conf.dataset_conf]
annotations_conf = self.exp_conf.annotations_conf
features_conf = self.exp_conf.features_conf
alerts_conf = None
if fold_id is None and kind != 'train':
alerts_conf = self.exp_conf.alerts_conf
return [
DetectionConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
alerts_conf,
name=exp_name,
parent=diadem_id,
fold_id=fold_id,
kind=kind) for dataset_conf in dataset_confs
]