def setExperimentFromArgs(self, args):
factory = DimensionReductionConfFactory.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):
conf = KmeansConfiguration(obj['num_clusters'])
if obj['projection_conf'] is not None:
projection_conf = DimensionReductionConfFactory.getFactory(
).fromJson(obj['projection_conf'])
conf.setDimensionReductionConf(projection_conf)
return conf
def fromJson(obj, logger=None):
conf = KmeansConfiguration(obj['num_clusters'], logger=logger)
if obj['projection_conf'] is not None:
proj_factory = DimensionReductionConfFactory.getFactory()
projection_conf = proj_factory.fromJson(obj['projection_conf'],
logger=logger)
conf.setDimensionReductionConf(projection_conf)
return conf
def fromJson(obj, session):
conf = DimensionReductionConfFactory.getFactory().fromJson(obj['conf'])
experiment = ProjectionExperiment(obj['project'],
obj['dataset'],
session,
create=False)
DimensionReductionExperiment.expParamFromJson(experiment, obj, conf)
return experiment
def fromJson(obj):
conf = ClusteringConfiguration(obj['num_clusters'],
num_results=obj['num_results'],
label=obj['label'])
if obj['projection_conf'] is not None:
projection_conf = DimensionReductionConfFactory.getFactory(
).fromJson(obj['projection_conf'])
conf.setDimensionReductionConf(projection_conf)
return conf
def fromJson(obj):
conf = GaussianMixtureConfiguration(obj['num_clusters'])
conf.covariance_type = obj['covariance_type']
conf.init_params = obj['init_params']
conf.max_iter = obj['max_iter']
if obj['projection_conf'] is not None:
projection_conf = DimensionReductionConfFactory.getFactory(
).fromJson(obj['projection_conf'])
conf.setDimensionReductionConf(projection_conf)
return conf
def generateParser():
parser = argparse.ArgumentParser(
description='Projection of the data for data visualization.')
DimensionReductionExperiment.projectDatasetFeturesParser(parser)
DimensionReductionExperiment.generateDimensionReductionParser(parser)
algos = ['Pca', 'Rca', 'Lda', 'Lmnn', 'Nca', 'Itml']
subparsers = parser.add_subparsers(dest='algo')
factory = DimensionReductionConfFactory.getFactory()
for algo in algos:
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateParser():
parser = argparse.ArgumentParser(description='Feature Selection.')
algos = [
'ChiSquare', 'Fclassif', 'MutualInfoClassif', 'VarianceFilter',
'RecursiveFeatureElimination', 'NoneFilter'
]
DimensionReductionExperiment.projectDatasetFeturesParser(parser)
DimensionReductionExperiment.generateDimensionReductionParser(parser)
subparsers = parser.add_subparsers(dest='algo')
factory = DimensionReductionConfFactory.getFactory()
for algo in algos:
algo_parser = subparsers.add_parser(algo)
factory.generateParser(algo, algo_parser)
return parser
def generateParamsFromArgs(args):
# DimensionReduction parameters
projection_conf = None
if args.projection_algo is not None:
projection_args = {}
projection_args['families_supervision'] = args.families_supervision
projection_args['num_components'] = None
projection_conf = DimensionReductionConfFactory.getFactory(
).fromParam(args.projection_algo, projection_args)
# Clustering parameters
params = {}
params['num_clusters'] = args.num_clusters
params['num_results'] = 5
params['projection_conf'] = projection_conf
params['label'] = args.label
return params
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with SecuML. If not, see <http://www.gnu.org/licenses/>.
from SecuML.core.DimensionReduction.Algorithms.FeatureSelection.VarianceFilter import VarianceFilter
from SecuML.core.DimensionReduction.Configuration import DimensionReductionConfFactory
from .UnsupervisedFeatureSelectionConfiguration import UnsupervisedFeatureSelectionConfiguration
class VarianceFilterConfiguration(UnsupervisedFeatureSelectionConfiguration):
def __init__(self, logger=None):
UnsupervisedFeatureSelectionConfiguration.__init__(self, VarianceFilter,
logger=logger)
@staticmethod
def fromJson(obj):
conf = VarianceFilterConfiguration()
return conf
def toJson(self):
conf = UnsupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'VarianceFilterConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass('VarianceFilterConfiguration',
VarianceFilterConfiguration)
def fromJson(obj, secuml_conf):
conf = DimensionReductionConfFactory.getFactory().fromJson(obj['conf'])
experiment = FeatureSelectionExperiment(secuml_conf)
experiment.initExperiment(obj['project'], obj['dataset'], create=False)
DimensionReductionExperiment.expParamFromJson(experiment, obj, conf)
return experiment
from .SemiSupervisedFeatureSelectionConfiguration import SemiSupervisedFeatureSelectionConfiguration
class ChiSquareConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self,
num_components=None,
families_supervision=None,
logger=None):
SemiSupervisedFeatureSelectionConfiguration.__init__(
self,
ChiSquare,
num_components=num_components,
families_supervision=families_supervision,
logger=logger)
@staticmethod
def fromJson(obj):
conf = ChiSquareConfiguration(
num_components=obj['num_components'],
families_supervision=obj['families_supervision'])
return conf
def toJson(self):
conf = SemiSupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'ChiSquareConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'ChiSquareConfiguration', ChiSquareConfiguration)
params = SemiSupervisedFeatureSelectionConfiguration.generateParamsFromArgs(
args)
params['step'] = args.step
if args.model == 'LogisticRegression':
model = LogisticRegression()
elif args.model == 'Svc':
model = SVC(kernel='linear')
elif args.model == 'DecisionTree':
model = DecisionTreeClassifier()
elif args.model == 'RandomForest':
model = RandomForestClassifier()
params['model'] = model
return params
@staticmethod
def generateParser(parser):
SemiSupervisedFeatureSelectionConfiguration.generateParser(parser)
parser.add_argument('--step', type=int, default=1)
parser.add_argument('--model',
type=str,
choices=[
'LogisticRegression', 'Svc', 'DecisionTree',
'RandomForest'
],
default='LogisticRegression')
DimensionReductionConfFactory.getFactory().registerClass(
'RecursiveFeatureEliminationConfiguration',
RecursiveFeatureEliminationConfiguration)
class FclassifConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self,
num_components=None,
families_supervision=None,
logger=None):
SemiSupervisedFeatureSelectionConfiguration.__init__(
self,
Fclassif,
num_components=num_components,
families_supervision=families_supervision,
logger=logger)
@staticmethod
def fromJson(obj, logger=None):
conf = FclassifConfiguration(
num_components=obj['num_components'],
families_supervision=obj['families_supervision'],
logger=logger)
return conf
def toJson(self):
conf = SemiSupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'FclassifConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'FclassifConfiguration', FclassifConfiguration)
# with SecuML. If not, see <http://www.gnu.org/licenses/>.
from SecuML.core.DimensionReduction.Algorithms.FeatureSelection.NoneFilter \
import NoneFilter
from SecuML.core.DimensionReduction.Configuration \
import DimensionReductionConfFactory
from .UnsupervisedFeatureSelectionConfiguration \
import UnsupervisedFeatureSelectionConfiguration
class NoneFilterConfiguration(UnsupervisedFeatureSelectionConfiguration):
def __init__(self, logger=None):
UnsupervisedFeatureSelectionConfiguration.__init__(self,
NoneFilter,
logger=logger)
@staticmethod
def fromJson(obj, logger=None):
conf = NoneFilterConfiguration(logger=logger)
return conf
def toJson(self):
conf = UnsupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'NoneFilterConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'NoneFilterConfiguration', NoneFilterConfiguration)
from SecuML.core.DimensionReduction.Configuration \
import DimensionReductionConfFactory
from .SemiSupervisedProjectionConfiguration \
import SemiSupervisedProjectionConfiguration
class SdmlConfiguration(SemiSupervisedProjectionConfiguration):
def __init__(self, families_supervision=None, logger=None):
SemiSupervisedProjectionConfiguration.__init__(
self,
Sdml,
families_supervision=families_supervision,
logger=logger)
@staticmethod
def fromJson(obj, logger=None):
conf = SdmlConfiguration(
families_supervision=obj['families_supervision'], logger=logger)
conf.num_components = obj['num_components']
return conf
def toJson(self):
conf = SemiSupervisedProjectionConfiguration.toJson(self)
conf['__type__'] = 'SdmlConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'SdmlConfiguration', SdmlConfiguration)
class MutualInfoClassifConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self, num_components=None, families_supervision=None,
logger=None):
SemiSupervisedFeatureSelectionConfiguration.__init__(
self,
MutualInfoClassif,
num_components=num_components,
families_supervision=families_supervision,
logger=logger)
@staticmethod
def fromJson(obj, logger=None):
conf = MutualInfoClassifConfiguration(
num_components=obj['num_components'],
families_supervision=obj['families_supervision'],
logger=logger)
return conf
def toJson(self):
conf = SemiSupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'MutualInfoClassifConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'MutualInfoClassifConfiguration',
MutualInfoClassifConfiguration)