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.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.common_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)
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.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 = 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