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