def setExperimentFromArgs(self, args):
self.setFeaturesFilenames(args.features_files)
factory = DimensionReductionConfFactory.getFactory()
conf = factory.fromArgs(args.algo, args)
self.setConf(conf)
self.initLabels(args.labels_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, session):
conf = DimensionReductionConfFactory.getFactory().fromJson(obj['conf'])
experiment = FeatureSelectionExperiment(obj['project'], obj['dataset'],
session)
DimensionReductionExperiment.expParamFromJson(experiment, obj)
experiment.setConf(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
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## 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.DimensionReduction.Algorithms.FeatureSelection.VarianceFilter import VarianceFilter
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from UnsupervisedFeatureSelectionConfiguration import UnsupervisedFeatureSelectionConfiguration
class VarianceFilterConfiguration(UnsupervisedFeatureSelectionConfiguration):
def __init__(self):
UnsupervisedFeatureSelectionConfiguration.__init__(
self, VarianceFilter)
@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)
##
## 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.DimensionReduction.Algorithms.Projection.Lmnn import Lmnn
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from SemiSupervisedProjectionConfiguration import SemiSupervisedProjectionConfiguration
class LmnnConfiguration(SemiSupervisedProjectionConfiguration):
def __init__(self, families_supervision=None):
SemiSupervisedProjectionConfiguration.__init__(
self, Lmnn, families_supervision=families_supervision)
@staticmethod
def fromJson(obj):
conf = LmnnConfiguration(
families_supervision=obj['families_supervision'])
conf.num_components = obj['num_components']
return conf
def toJson(self):
conf = SemiSupervisedProjectionConfiguration.toJson(self)
conf['__type__'] = 'LmnnConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'LmnnConfiguration', LmnnConfiguration)
## 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.DimensionReduction.Algorithms.FeatureSelection.MutualInfoClassif import MutualInfoClassif
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from SemiSupervisedFeatureSelectionConfiguration import SemiSupervisedFeatureSelectionConfiguration
class MutualInfoClassifConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self, num_components = None, families_supervision = None):
SemiSupervisedFeatureSelectionConfiguration.__init__(self, MutualInfoClassif,
num_components = num_components,
families_supervision = families_supervision)
@staticmethod
def fromJson(obj):
conf = MutualInfoClassifConfiguration(num_components = obj['num_components'],
families_supervision = obj['families_supervision'])
return conf
def toJson(self):
conf = SemiSupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'MutualInfoClassifConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass('MutualInfoClassifConfiguration',
MutualInfoClassifConfiguration)
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)
from SecuML.DimensionReduction.Algorithms.FeatureSelection.Fclassif import Fclassif
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from SemiSupervisedFeatureSelectionConfiguration import SemiSupervisedFeatureSelectionConfiguration
class FclassifConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self, num_components=None, families_supervision=None):
SemiSupervisedFeatureSelectionConfiguration.__init__(
self,
Fclassif,
num_components=num_components,
families_supervision=families_supervision)
@staticmethod
def fromJson(obj):
conf = FclassifConfiguration(
num_components=obj['num_components'],
families_supervision=obj['families_supervision'])
return conf
def toJson(self):
conf = SemiSupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'FclassifConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'FclassifConfiguration', FclassifConfiguration)
## 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.DimensionReduction.Algorithms.FeatureSelection.ChiSquare import ChiSquare
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from SemiSupervisedFeatureSelectionConfiguration import SemiSupervisedFeatureSelectionConfiguration
class ChiSquareConfiguration(SemiSupervisedFeatureSelectionConfiguration):
def __init__(self, num_components = None, families_supervision = None):
SemiSupervisedFeatureSelectionConfiguration.__init__(self, ChiSquare,
num_components = num_components,
families_supervision = families_supervision)
@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)
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## 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.DimensionReduction.Algorithms.FeatureSelection.NoneFilter import NoneFilter
from SecuML.DimensionReduction.Configuration import DimensionReductionConfFactory
from UnsupervisedFeatureSelectionConfiguration import UnsupervisedFeatureSelectionConfiguration
class NoneFilterConfiguration(UnsupervisedFeatureSelectionConfiguration):
def __init__(self):
UnsupervisedFeatureSelectionConfiguration.__init__(self, NoneFilter)
@staticmethod
def fromJson(obj):
conf = NoneFilterConfiguration()
return conf
def toJson(self):
conf = UnsupervisedFeatureSelectionConfiguration.toJson(self)
conf['__type__'] = 'NoneFilterConfiguration'
return conf
DimensionReductionConfFactory.getFactory().registerClass(
'NoneFilterConfiguration', NoneFilterConfiguration)