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