def from_args(args):
secuml_conf = ExpConf.common_from_args(args)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
core_conf = projection_conf.get_factory().from_args(args.algo, args,
secuml_conf.logger)
return ProjectionConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf, name=args.exp_name)
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
logger = secuml_conf.logger
dataset_conf = DatasetConf.from_args(args, logger)
features_conf = FeaturesConf.from_args(args, logger)
annotations_conf = AnnotationsConf(args.annotations_file, None, logger)
core_conf = strategies_conf.get_factory().from_args('Rcd', args,
logger)
return RcdConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf, name=args.exp_name)
def from_args(args):
secuml_conf = ExpConf.common_from_args(args)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
return FeaturesAnalysisConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
None,
name=args.exp_name)
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
core_conf = strategies_conf.get_factory().from_args(args.strategy,
args,
secuml_conf.logger)
return ActiveLearningConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf,
name=args.exp_name)
def from_args(args):
secuml_conf = ExpConf.common_from_args(args)
already_trained = None
core_conf = ClassificationConf.from_args(args, secuml_conf.logger)
if args.model_class != 'AlreadyTrained':
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
already_trained = args.model_exp_id
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
return DiademConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf, name=args.exp_name,
already_trained=already_trained)
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
core_conf = clustering_conf.get_factory().from_args(
args.algo, args, secuml_conf.logger)
conf = ClusteringConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
name=args.exp_name,
label=args.label)
return conf
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
classif_conf = ClassificationConf.from_args(args, secuml_conf.logger)
model_class = classifiers.get_factory().get_class(args.model_class)
classifier_type = get_classifier_type(model_class)
if classifier_type in [
ClassifierType.supervised, ClassifierType.semisupervised
]:
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
already_trained = None
if args.model_class == 'AlreadyTrained':
already_trained = args.model_exp_id
alerts_conf = AlertsConf.from_args(args, secuml_conf.logger)
if (classifier_type == ClassifierType.unsupervised
and alerts_conf.classifier_conf is not None):
raise InvalidInputArguments('Supervised classification of the '
'alerts is not supported for '
'unsupervised model classes. ')
if classif_conf.classifier_conf.multiclass:
if alerts_conf.with_analysis():
raise InvalidInputArguments('Alerts analysis is not supported '
'for multiclass models. ')
else:
alerts_conf = None
if (classif_conf.test_conf.method == 'dataset'
and classif_conf.test_conf.streaming
and alerts_conf.with_analysis()):
raise InvalidInputArguments('Alerts analysis is not supported '
'in streaming mode. ')
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
if (features_conf.sparse
and not classif_conf.classifier_conf.accept_sparse):
raise InvalidInputArguments('%s does not support sparse '
'features. ' % args.model_class)
return DiademConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
classif_conf,
alerts_conf,
name=args.exp_name,
already_trained=already_trained,
no_training_detection=args.no_training_detection)
def from_args(args):
if args.annotations_file is None and args.multiclass:
raise InvalidInputArguments('--annotations <file> is required. '
'An annotation file must be specified '
'to group the instances according to '
'their families.')
secuml_conf = ExpConf.secuml_conf_from_args(args)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
return FeaturesAnalysisConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
args.multiclass,
name=args.exp_name)
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
classif_conf = ClassificationConf.from_args(args, secuml_conf.logger)
model_class = classifiers.get_factory().get_class(args.model_class)
classifier_type = get_classifier_type(model_class)
if classifier_type in [ClassifierType.supervised,
ClassifierType.semisupervised]:
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
already_trained = None
if args.model_class == 'AlreadyTrained':
already_trained = args.model_exp_id
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
return DiademConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, classif_conf, name=args.exp_name,
already_trained=already_trained)
