def fromJson(obj):
validation_conf = None
if obj['validation_conf'] is not None:
validation_conf = ValidationDatasetConf.fromJson(
obj['validation_conf'])
conf = GornitzConfiguration(obj['auto'], obj['budget'], obj['batch'],
validation_conf)
return conf
def fromJson(obj):
validation_conf = None
if obj['validation_conf'] is not None:
validation_conf = ValidationDatasetConf.fromJson(
obj['validation_conf'])
binary_model_conf = ClassifierConfFactory.getFactory().fromJson(
obj['models_conf']['binary'])
conf = RandomSamplingConfiguration(obj['auto'], obj['budget'], obj['batch'],
binary_model_conf, validation_conf)
return conf
def fromJson(obj):
validation_conf = None
if obj['validation_conf'] is not None:
validation_conf = ValidationDatasetConf.fromJson(
obj['validation_conf'])
binary_model_conf = ClassifierConfFactory.getFactory().fromJson(
obj['models_conf']['binary'])
conf = AladinConfiguration(obj['auto'], obj['budget'],
obj['num_annotations'], binary_model_conf,
validation_conf)
return conf
def fromJson(obj):
validation_conf = None
if obj['validation_conf'] is not None:
validation_conf = ValidationDatasetConf.fromJson(
obj['validation_conf'])
multiclass_model_conf = ClassifierConfFactory.getFactory().fromJson(
obj['models_conf']['multiclass'])
rare_category_detection_conf = RareCategoryDetectionStrategy.fromJson(
obj['rare_category_detection_conf'])
conf = RareCategoryDetectionConfiguration(
obj['auto'], obj['budget'], rare_category_detection_conf,
multiclass_model_conf, validation_conf)
return conf
def fromJson(obj):
validation_conf = None
if obj['validation_conf'] is not None:
validation_conf = ValidationDatasetConf.fromJson(
obj['validation_conf'])
rare_category_detection_conf = RareCategoryDetectionStrategy.fromJson(
obj['rare_category_detection_conf'])
binary_model_conf = ClassifierConfFactory.getFactory().fromJson(
obj['models_conf']['binary'])
conf = IlabConfiguration(obj['auto'], obj['budget'],
rare_category_detection_conf,
obj['num_uncertain'], obj['eps'],
binary_model_conf,
validation_conf)
return conf
def generateParamsFromArgs(args, binary_model_conf=None):
if binary_model_conf is None:
supervised_args = {}
supervised_args['num_folds'] = args.num_folds
supervised_args['sample_weight'] = args.sample_weight
supervised_args['families_supervision'] = False
supervised_args['test_conf'] = UnlabeledLabeledConf()
binary_model_conf = ClassifierConfFactory.getFactory().fromParam(
args.model_class, supervised_args)
active_learning_params = {}
active_learning_params['auto'] = args.auto
active_learning_params['budget'] = args.budget
active_learning_params['binary_model_conf'] = binary_model_conf
validation_conf = None
if args.validation_dataset is not None:
validation_conf = ValidationDatasetConf(args.validation_dataset)
active_learning_params['validation_conf'] = validation_conf
return active_learning_params
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