def gen_parser():
parser = argparse.ArgumentParser(
description='Learn a detection model. '
'The ground-truth must be stored in '
'annotations/ground_truth.csv.')
ExpConf.gen_parser(parser)
ClassificationConf.gen_parser(parser)
factory = classifiers.get_factory()
models = factory.get_methods()
models.remove('AlreadyTrained')
subparsers = parser.add_subparsers(dest='model_class')
subparsers.required = True
for model in models:
model_parser = subparsers.add_parser(model)
factory.gen_parser(model, model_parser)
classifier_type = get_classifier_type(factory.get_class(model))
if classifier_type in [ClassifierType.supervised,
ClassifierType.semisupervised]:
AnnotationsConf.gen_parser(
model_parser, required=False,
message='CSV file containing the annotations of '
'some or all the instances.')
# Add subparser for already trained model
already_trained = subparsers.add_parser('AlreadyTrained')
factory.gen_parser('AlreadyTrained', already_trained)
return parser
def get_naive_bayes_conf(self):
name = '-'.join([
'AL%d' % self.exp.exp_id,
'Iter%d' % self.iteration.iter_num, 'all', 'NaiveBayes'
])
classifier_conf = self.exp.exp_conf.core_conf.classifier_conf
optim_conf = classifier_conf.hyperparam_conf.optim_conf
multiclass = True
factory = classifiers.get_factory()
naive_bayes_conf = factory.get_default('GaussianNaiveBayes',
optim_conf.num_folds,
optim_conf.n_jobs, multiclass,
self.exp.logger)
test_conf = UnlabeledLabeledConf(self.exp.logger)
classification_conf = ClassificationConf(naive_bayes_conf, test_conf,
self.exp.logger)
features_conf = FeaturesConf(
self.exp.exp_conf.features_conf.input_features,
self.exp.exp_conf.features_conf.sparse,
self.exp.exp_conf.features_conf.logger,
filter_in_f=self.exp.exp_conf.features_conf.filter_in_f,
filter_out_f=self.exp.exp_conf.features_conf.filter_out_f)
exp_conf = DiademConf(self.exp.exp_conf.secuml_conf,
self.exp.exp_conf.dataset_conf,
features_conf,
self.exp.exp_conf.annotations_conf,
classification_conf,
None,
name=name,
parent=self.exp.exp_id)
DiademExp(exp_conf, session=self.exp.session)
return naive_bayes_conf
def get_naive_bayes_conf(self):
name = '-'.join([
'AL%d' % self.exp.exp_id,
'Iter%d' % self.iteration.iter_num, 'all', 'NaiveBayes'
])
classifier_conf = self.exp.exp_conf.core_conf.classifier_conf
optim_conf = classifier_conf.hyperparam_conf.optim_conf
multiclass = True
hyperparam_conf = HyperparamConf.get_default(
optim_conf.num_folds, optim_conf.n_jobs, multiclass,
GaussianNaiveBayesConf._get_hyper_desc(), self.exp.logger)
naive_bayes_conf = GaussianNaiveBayesConf(multiclass, hyperparam_conf,
self.exp.logger)
test_conf = UnlabeledLabeledConf(self.exp.logger, None)
classification_conf = ClassificationConf(naive_bayes_conf, test_conf,
self.exp.logger)
exp_conf = DiademConf(self.exp.exp_conf.secuml_conf,
self.exp.exp_conf.dataset_conf,
self.exp.exp_conf.features_conf,
self.exp.exp_conf.annotations_conf,
classification_conf,
name=name,
parent=self.exp.exp_id)
naive_bayes_exp = DiademExp(exp_conf, session=self.exp.session)
naive_bayes_exp.create_exp()
return naive_bayes_conf
def _create_naive_bayes_conf(self):
name = '-'.join([
'AL%d' % (self.exp.exp_id),
'Iter%d' % (self.iteration.iter_num), 'all', 'NaiveBayes'
])
multiclass_model = self.exp.exp_conf.core_conf.multiclass_model
classifier_conf = multiclass_model.classifier_conf
optim_conf = classifier_conf.hyperparam_conf.optim_conf
multiclass = True
factory = classifiers.get_factory()
naive_bayes_conf = factory.get_default('GaussianNaiveBayes',
optim_conf.num_folds,
optim_conf.n_jobs, multiclass,
self.exp.logger)
test_conf = UnlabeledLabeledConf(self.exp.logger)
classif_conf = ClassificationConf(naive_bayes_conf, test_conf,
self.exp.logger)
DiademConf(self.exp.exp_conf.secuml_conf,
self.exp.exp_conf.dataset_conf,
self.exp.exp_conf.features_conf,
self.exp.exp_conf.annotations_conf,
classif_conf,
None,
name=name,
parent=self.exp.exp_id)
return naive_bayes_conf
def _rcd_conf(args, logger):
factory = classifiers.get_factory()
classifier_conf = factory.get_default('LogisticRegression', None, None,
True, logger)
classif_conf = ClassificationConf(classifier_conf,
UnlabeledLabeledConf(logger), logger)
return RcdStrategyConf(classif_conf, args.cluster_strategy,
args.num_annotations, 'uniform', logger)
def _get_main_model_conf(self, validation_conf, logger):
hyperparam_conf = HyperparamConf.get_default(None, None, False, None,
logger)
classifier_conf = SssvddConf(hyperparam_conf, logger)
return ClassificationConf(classifier_conf,
UnlabeledLabeledConf(logger),
logger,
validation_conf=validation_conf)
def _get_lr_conf(self, validation_conf, logger, multiclass=False):
factory = classifiers.get_factory()
classifier_conf = factory.get_default('LogisticRegression', None, None,
multiclass, logger)
return ClassificationConf(classifier_conf,
UnlabeledLabeledConf(logger),
logger,
validation_conf=validation_conf)
def from_json(self, obj, logger):
class_name = obj['__type__']
main_model = ClassificationConf.from_json(obj['main_model_conf'],
logger)
validation_conf = None
if obj['validation_conf'] is None:
return None
validation_conf = TestDatasetConf(logger, None, obj['validation_conf'])
return self.methods[class_name].from_json(obj, main_model,
validation_conf, logger)
def _get_lr_conf(self, validation_conf, logger, multiclass=False):
hyperparam_conf = HyperparamConf.get_default(
None, None, multiclass,
LogisticRegressionConf._get_hyper_desc(),
logger)
core_conf = LogisticRegressionConf(multiclass, 'liblinear',
hyperparam_conf, logger)
return ClassificationConf(core_conf,
UnlabeledLabeledConf(logger, None),
logger, validation_conf=validation_conf)
def gen_parser():
parser = argparse.ArgumentParser(
description='Train and evaluate a detection '
'model. ')
ExpConf.gen_parser(parser, sparse=True)
parser.add_argument('--no-training-detection',
action='store_true',
default=False,
help='''When specified, the detection model is
not applied to the training instances. ''')
factory = classifiers.get_factory()
models = factory.get_methods()
models.remove('AlreadyTrained')
subparsers = parser.add_subparsers(dest='model_class')
subparsers.required = True
for model in models:
model_parser = subparsers.add_parser(model)
factory.gen_parser(model, model_parser)
classifier_type = get_classifier_type(factory.get_class(model))
if classifier_type in [
ClassifierType.supervised, ClassifierType.semisupervised
]:
default = None
message = '''CSV file containing the annotations of some
instances, or GROUND_TRUTH to use the ground
truth annotations stored in idents.csv. '''
if classifier_type == ClassifierType.supervised:
default = 'GROUND_TRUTH'
message = '%s Default: GROUND_TRUTH.' % message
AnnotationsConf.gen_parser(model_parser,
required=default is None,
default=default,
message=message)
ClassificationConf.gen_parser(model_parser)
AlertsConf.gen_parser(model_parser)
# Add subparser for already trained model
already_trained = subparsers.add_parser('AlreadyTrained')
factory.gen_parser('AlreadyTrained', already_trained)
ClassificationConf.gen_parser(already_trained)
AlertsConf.gen_parser(already_trained)
return parser
def _rcd_conf(args, logger):
hyperparam_conf = HyperparamConf.get_default(
None, None, True,
LogisticRegressionConf._get_hyper_desc(),
logger)
core_conf = LogisticRegressionConf(True, 'liblinear', hyperparam_conf,
logger)
classif_conf = ClassificationConf(core_conf,
UnlabeledLabeledConf(logger, None),
logger)
return RcdStrategyConf(classif_conf, args.cluster_strategy,
args.num_annotations, 'uniform', logger)
def from_args(args):
secuml_conf = ExpConf.common_from_args(args)
already_trained = None
core_conf = ClassificationConf.from_args(args, secuml_conf.logger)
if args.model_class != 'AlreadyTrained':
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
already_trained = args.model_exp_id
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
return DiademConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf, name=args.exp_name,
already_trained=already_trained)
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
classif_conf = ClassificationConf.from_args(args, secuml_conf.logger)
model_class = classifiers.get_factory().get_class(args.model_class)
classifier_type = get_classifier_type(model_class)
if classifier_type in [
ClassifierType.supervised, ClassifierType.semisupervised
]:
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
already_trained = None
if args.model_class == 'AlreadyTrained':
already_trained = args.model_exp_id
alerts_conf = AlertsConf.from_args(args, secuml_conf.logger)
if (classifier_type == ClassifierType.unsupervised
and alerts_conf.classifier_conf is not None):
raise InvalidInputArguments('Supervised classification of the '
'alerts is not supported for '
'unsupervised model classes. ')
if classif_conf.classifier_conf.multiclass:
if alerts_conf.with_analysis():
raise InvalidInputArguments('Alerts analysis is not supported '
'for multiclass models. ')
else:
alerts_conf = None
if (classif_conf.test_conf.method == 'dataset'
and classif_conf.test_conf.streaming
and alerts_conf.with_analysis()):
raise InvalidInputArguments('Alerts analysis is not supported '
'in streaming mode. ')
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
if (features_conf.sparse
and not classif_conf.classifier_conf.accept_sparse):
raise InvalidInputArguments('%s does not support sparse '
'features. ' % args.model_class)
return DiademConf(secuml_conf,
dataset_conf,
features_conf,
annotations_conf,
classif_conf,
alerts_conf,
name=args.exp_name,
already_trained=already_trained,
no_training_detection=args.no_training_detection)
def from_args(self, method, args, logger):
validation_conf = None
if args.validation_datasets is not None:
validation_conf = ValidationDatasetsConf.from_args(args, logger)
class_ = self.get_class(method)
main_model_type = class_.main_model_type()
main_model_conf = None
if main_model_type is not None:
factory = classifiers.get_factory()
args.multiclass = main_model_type == 'multiclass'
classifier_conf = factory.from_args(args.model_class, args, logger)
test_conf = UnlabeledLabeledConf(logger)
main_model_conf = ClassificationConf(
classifier_conf, test_conf,
logger,
validation_conf=validation_conf)
return class_.from_args(args, main_model_conf, validation_conf, logger)
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 = ClassificationConf.from_json(conf_json['core_conf'],
secuml_conf.logger)
exp_conf = DiademConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, core_conf,
name=conf_json['name'],
parent=conf_json['parent'],
already_trained=conf_json['already_trained'])
exp_conf.exp_id = conf_json['exp_id']
return exp_conf
def from_args(args):
secuml_conf = ExpConf.secuml_conf_from_args(args)
classif_conf = ClassificationConf.from_args(args, secuml_conf.logger)
model_class = classifiers.get_factory().get_class(args.model_class)
classifier_type = get_classifier_type(model_class)
if classifier_type in [ClassifierType.supervised,
ClassifierType.semisupervised]:
annotations_conf = AnnotationsConf(args.annotations_file, None,
secuml_conf.logger)
else:
annotations_conf = AnnotationsConf(None, None, secuml_conf.logger)
already_trained = None
if args.model_class == 'AlreadyTrained':
already_trained = args.model_exp_id
dataset_conf = DatasetConf.from_args(args, secuml_conf.logger)
features_conf = FeaturesConf.from_args(args, secuml_conf.logger)
return DiademConf(secuml_conf, dataset_conf, features_conf,
annotations_conf, classif_conf, name=args.exp_name,
already_trained=already_trained)
def from_json(obj, logger):
classif_conf = ClassificationConf.from_json(obj['classification_conf'],
logger)
return RcdStrategyConf(classif_conf, obj['cluster_strategy'],
obj['num_annotations'], obj['cluster_weights'],
logger)