def setExperimentFromArgs(self, args):
factory = ClusteringConfFactory.getFactory()
conf = factory.fromArgs(args.algo, args, logger=self.logger)
self.setConf(conf,
args.features_file,
annotations_filename=args.annotations_file)
self.export()
def fromJson(obj, secuml_conf):
conf = ClusteringConfFactory.getFactory().fromJson(obj['conf'])
experiment = ClusteringExperiment(secuml_conf)
experiment.initExperiment(obj['project'], obj['dataset'],
create=False)
Experiment.expParamFromJson(experiment, obj, conf)
return experiment
def fromJson(obj, logger=None):
clustering_conf = ClusteringConfFactory.getFactory().fromJson(
obj['clustering_conf'], logger=logger)
conf = AlertsConfiguration(obj['num_max_alerts'],
obj['detection_threshold'],
clustering_conf,
logger=logger)
return conf
def setExperimentFromArgs(self, args):
self.initExperiment(args.project, args.dataset,
experiment_name=args.exp_name)
factory = ClusteringConfFactory.getFactory()
conf = factory.fromArgs(args.algo, args, logger=self.logger)
self.setConf(conf, args.features_file,
annotations_filename=args.annotations_file)
self.export()
def generateParser():
parser = argparse.ArgumentParser(
description='Clustering of the data for data exploration.')
Experiment.projectDatasetFeturesParser(parser)
algos = ['Kmeans', 'GaussianMixture']
subparsers = parser.add_subparsers(dest='algo')
factory = ClusteringConfFactory.getFactory()
for algo in algos:
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateAlertConfFromArgs(args):
params = {}
params['num_clusters'] = args.num_clusters
params['num_results'] = None
params['projection_conf'] = None
params['label'] = 'all'
clustering_conf = ClusteringConfFactory.getFactory().fromParam(
args.clustering_algo, params)
alerts_conf = AlertsConfiguration(args.top_n_alerts,
args.detection_threshold,
clustering_conf)
return alerts_conf
def generateParser():
parser = argparse.ArgumentParser(
description='Clustering of the data for data exploration.')
Experiment.projectDatasetFeturesParser(parser)
subparsers = parser.add_subparsers(dest='algo')
subparsers.required = True
factory = ClusteringConfFactory.getFactory()
algos = factory.getAlgorithms()
for algo in algos:
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateAlreadyTrainedConf(self, factory, args, logger):
conf_filename = self.checkAlreadyTrainedConf(args)
with open(conf_filename, 'r') as f:
conf_json = json.load(f)
conf = factory.fromJson(conf_json['classification_conf'],
logger=logger)
params = {}
params['num_clusters'] = args.num_clusters
params['num_results'] = None
params['projection_conf'] = None
params['label'] = 'all'
clustering_conf = ClusteringConfFactory.getFactory().fromParam(
args.clustering_algo, params, logger=logger)
alerts_conf = AlertsConfiguration(args.top_n_alerts,
args.detection_threshold,
clustering_conf,
logger=logger)
test_conf = ValidationDatasetConf(args.validation_dataset,
alerts_conf=alerts_conf,
logger=logger)
conf.test_conf = test_conf
return conf