def fromArgs(args):
secuml_conf = ExpConf.common_from_args(args)
dataset_conf = DatasetConf.fromArgs(args, secuml_conf.logger)
features_conf = FeaturesConf.fromArgs(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,
experiment_name=args.exp_name)
def fromArgs(args):
secuml_conf = ExpConf.common_from_args(args)
dataset_conf = DatasetConf.fromArgs(args, secuml_conf.logger)
features_conf = FeaturesConf.fromArgs(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
factory = ActiveLearningConfFactory.getFactory()
core_conf = factory.fromArgs(args.strategy, args, secuml_conf.logger)
return ActiveLearningConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=args.exp_name)
def fromArgs(args):
secuml_conf = ExpConf.common_from_args(args)
dataset_conf = DatasetConf.fromArgs(args, secuml_conf.logger)
features_conf = FeaturesConf.fromArgs(args, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
factory = ClusteringConfFactory.getFactory()
core_conf = factory.fromArgs(args.algo, args, secuml_conf.logger)
conf = ClusteringConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=args.exp_name,
label=args.label)
return conf
def fromArgs(args):
secuml_conf = ExpConf.common_from_args(args)
already_trained = None
if args.model_class != 'AlreadyTrained':
core_conf = CoreClassificationConf.fromArgs(
args, True, secuml_conf.logger)
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
already_trained = args.model_exp_id
core_conf = CoreClassificationConf.fromArgs(
args, False, secuml_conf.logger)
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
dataset_conf = DatasetConf.fromArgs(args, secuml_conf.logger)
features_conf = FeaturesConf.fromArgs(args, secuml_conf.logger)
return ClassificationConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=args.exp_name,
already_trained=already_trained)