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 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)
exp_conf = ValidationConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
None,
experiment_name=conf_json['experiment_name'],
parent=conf_json['parent'])
exp_conf.experiment_id = conf_json['experiment_id']
return exp_conf
def createTestExperiment(self):
self.test_exp = None
test_conf = self.exp_conf.core_conf.validation_conf
if test_conf is not None:
logger = self.exp_conf.secuml_conf.logger
annotations_conf = AnnotationsConf('ground_truth.csv', None,
logger)
dataset_conf = DatasetConf(self.exp_conf.dataset_conf.project,
test_conf.test_dataset, logger)
features_conf = FeaturesConf(
self.exp_conf.features_conf.input_features, logger)
validation_conf = ValidationConf(self.exp_conf.secuml_conf,
dataset_conf, features_conf,
annotations_conf, None)
self.test_exp = ValidationExperiment(validation_conf,
session=self.session)
self.test_exp.run()
self.exp_conf.test_exp_conf = validation_conf
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)
factory = ActiveLearningConfFactory.getFactory()
core_conf = factory.from_json(conf_json['core_conf'],
secuml_conf.logger)
conf = ActiveLearningConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=conf_json['experiment_name'],
parent=conf_json['parent'])
conf.experiment_id = conf_json['experiment_id']
return conf
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 = CoreClassificationConf.from_json(conf_json['core_conf'],
secuml_conf.logger)
exp_conf = ClassificationConf(
secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=conf_json['experiment_name'],
parent=conf_json['parent'],
already_trained=conf_json['already_trained'])
exp_conf.experiment_id = conf_json['experiment_id']
return exp_conf
def createClusteringExp(self, core_clustering_conf):
exp_conf = self.diadem_exp.exp_conf
features_conf = FeaturesConf(exp_conf.features_conf.input_features,
exp_conf.secuml_conf.logger)
if self.diadem_exp.test_exp is not None:
dataset_conf = exp_conf.test_exp_conf.dataset_conf
annotations_conf = exp_conf.test_exp_conf.annotations_conf
else:
dataset_conf = exp_conf.dataset_conf
annotations_conf = exp_conf.annotations_conf
clustering_exp_conf = ClusteringConf(
exp_conf.secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_clustering_conf,
experiment_name=None,
parent=self.diadem_exp.experiment_id)
return ClusteringExperiment(clustering_exp_conf,
create=True,
session=self.diadem_exp.session)
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)
factory = ClusteringConfFactory.getFactory()
core_conf = None
if conf_json['core_conf'] is not None:
core_conf = factory.from_json(conf_json['core_conf'],
secuml_conf.logger)
exp_conf = ClusteringConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
core_conf,
experiment_name=conf_json['experiment_name'],
parent=conf_json['parent'],
label=conf_json['label'])
exp_conf.experiment_id = conf_json['experiment_id']
return exp_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)
def create_exp(self):
Experiment.create_exp(self)
# create projection experiment
self.projection_exp = None
if self.exp_conf.core_conf is None:
return
projection_core_conf = self.exp_conf.core_conf.projection_conf
if projection_core_conf is not None:
features_conf = FeaturesConf(
self.exp_conf.features_conf.input_features,
self.exp_conf.secuml_conf.logger)
projection_conf = ProjectionConf(self.exp_conf.secuml_conf,
self.exp_conf.dataset_conf,
features_conf,
self.exp_conf.annotations_conf,
projection_core_conf,
experiment_name='-'.join([
self.exp_conf.experiment_name,
'projection'
]),
parent=self.experiment_id)
self.projection_exp = ProjectionExperiment(projection_conf,
session=self.session)