def generateParser():
parser = argparse.ArgumentParser(
description='Clustering of the data for data exploration.')
ExpConf.generateParser(parser)
AnnotationsConf.generateParser(
parser,
message='CSV file containing the annotations of some '
'instances. These annotations are used for '
'semi-supervised projections.')
parser.add_argument(
'--label',
choices=['all', 'malicious', 'benign'],
default='all',
help='The clustering is built from all the instances in the '
'dataset, or only from the benign or malicious ones. '
'By default, the clustering is built from all the '
'instances. The malicious and benign instances are '
'selected according to the ground-truth stored in '
'annotations/ground_truth.csv.')
subparsers = parser.add_subparsers(dest='algo')
subparsers.required = True
factory = ClusteringConfFactory.getFactory()
for algo in factory.getMethods():
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateParser():
parser = argparse.ArgumentParser(
description='Learn a detection model. '
'The ground-truth must be stored in '
'annotations/ground_truth.csv.')
ExpConf.generateParser(parser)
CoreClassificationConf.generateParser(parser)
models = [
'LogisticRegression', 'Svc', 'GaussianNaiveBayes', 'DecisionTree',
'RandomForest', 'GradientBoosting'
]
subparsers = parser.add_subparsers(dest='model_class')
subparsers.required = True
factory = ClassifierConfFactory.getFactory()
for model in models:
model_parser = subparsers.add_parser(model)
factory.generateParser(model, model_parser)
AnnotationsConf.generateParser(
model_parser,
required=True,
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.generateParser('AlreadyTrained', already_trained)
return parser
def generateParser():
parser = argparse.ArgumentParser(description='Features Analysis')
ExpConf.generateParser(parser, filters=False)
AnnotationsConf.generateParser(parser,
required=True,
message='CSV file containing the annotations of some or all'
' the instances.')
return parser
def generateParser():
parser = argparse.ArgumentParser(
description='Rare Category Detection',
formatter_class=argparse.RawTextHelpFormatter)
ExpConf.generateParser(parser)
AnnotationsConf.generateParser(
parser,
default='init_annotations.csv',
required=False,
message='CSV file containing the initial annotations '
'used to learn the first supervised detection '
'model.')
factory = ActiveLearningConfFactory.getFactory()
factory.generateParser('RareCategoryDetection', parser)
return parser
def generateParser():
parser = argparse.ArgumentParser(
description='Projection of the data for data visualization.')
ExpConf.generateParser(parser)
AnnotationsConf.generateParser(
parser,
message='CSV file containing the annotations of some'
' instances. These annotations are used for '
'semi-supervised projections.')
algos = ['Pca', 'Rca', 'Lda', 'Lmnn', 'Nca', 'Itml']
subparsers = parser.add_subparsers(dest='algo')
subparsers.required = True
factory = ProjectionConfFactory.getFactory()
for algo in algos:
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateParser():
parser = argparse.ArgumentParser(
description='Active Learning',
formatter_class=argparse.RawTextHelpFormatter)
ExpConf.generateParser(parser)
AnnotationsConf.generateParser(
parser,
default='init_annotations.csv',
required=False,
message='CSV file containing the initial annotations '
'used to learn the first supervised detection '
'model.')
subparsers = parser.add_subparsers(dest='strategy')
subparsers.required = True
factory = ActiveLearningConfFactory.getFactory()
strategies = factory.getMethods()
for strategy in strategies:
strategy_parser = subparsers.add_parser(strategy)
factory.generateParser(strategy, strategy_parser)
return parser