def gen_parser():
parser = argparse.ArgumentParser(
description='Projection of the data for data visualization.')
ExpConf.gen_parser(parser)
AnnotationsConf.gen_parser(
parser,
message='CSV file containing the annotations of some'
' instances. These annotations are used for '
'semi-supervised projections.')
subparsers = parser.add_subparsers(dest='algo')
subparsers.required = True
for algo in projection_conf.get_factory().get_methods():
algo_parser = subparsers.add_parser(algo)
projection_conf.get_factory().gen_parser(algo, algo_parser)
return parser
def proj_conf_from_args(args, logger):
if (not hasattr(args, 'projection_algo')
or args.projection_algo is None):
return None
proj_factory = projection_conf.get_factory()
proj_conf = proj_factory.from_args(args.projection_algo, args, logger)
return proj_conf
def gen_parser(parser):
# Clustering arguments
parser.add_argument('--num-clusters',
type=int,
help='''Number of clusters. Default: 4.''',
default=4)
# Projection parameters
projection_group = parser.add_argument_group('Projection parameters')
projection_group.add_argument(
'--projection-algo',
choices=projection_conf.get_factory().get_methods() + [None],
default=None,
help='''Projection performed before building the clustering.
By default the instances are not projected.''')
projection_group.add_argument(
'--multiclass',
action='store_true',
default=False,
help='''When specified, the semi-supervision is based on
the families instead of the binary labels.
Useless if an unsupervised projection method is
used.''')
projection_group.add_argument(
'--num-components',
type=int,
default=None,
help='''Number of components. Default: None,
the number of components is set to the
number of input features. ''')
def from_json(self, obj, logger):
proj_conf = None
if obj['projection_conf'] is not None:
proj_factory = projection_conf.get_factory()
proj_conf = proj_factory.from_json(obj['projection_conf'], logger)
class_ = self.methods[obj['__type__']]
return class_.from_json(obj, proj_conf, logger)
def gen_parser():
parser = argparse.ArgumentParser(
description='Projection of the data for data visualization.')
ExpConf.gen_parser(parser)
AnnotationsConf.gen_parser(
parser,
message='''CSV file containing the annotations of some
instances, or GROUND_TRUTH to use the ground
truth annotations stored in idents.csv.
These annotations are used for semi-supervised
projections and are displayed in the GUI.''')
subparsers = parser.add_subparsers(dest='algo')
subparsers.required = True
for algo in projection_conf.get_factory().get_methods():
algo_parser = subparsers.add_parser(algo)
projection_conf.get_factory().gen_parser(algo, algo_parser)
return parser
def from_args(self, method, args, logger):
if (not hasattr(args, 'projection_algo')
or args.projection_algo is None):
proj_conf = None
else:
proj_factory = projection_conf.get_factory()
proj_conf = proj_factory.from_args(args.projection_algo, args,
logger)
class_ = self.methods[method + 'Conf']
return class_.from_args(args, proj_conf, logger)
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_json(conf_json, secuml_conf):
dataset_conf = DatasetConf.from_json(conf_json['dataset_conf'],
secuml_conf.logger)
features_conf = FeaturesConf.from_json(conf_json['features_conf'],
secuml_conf.logger)
annotations_conf = AnnotationsConf.from_json(
conf_json['annotations_conf'],
secuml_conf.logger)
core_conf = projection_conf.get_factory().from_json(
conf_json['core_conf'],
secuml_conf.logger)
conf = ProjectionConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf,
name=conf_json['name'],
parent=conf_json['parent'])
conf.exp_id = conf_json['exp_id']
return conf
