return conf
def toJson(self):
conf = ClassifierConfiguration.toJson(self)
conf['__type__'] = 'SvcConfiguration'
conf['c'] = self.c.toJson()
return conf
def probabilistModel(self):
return False
def semiSupervisedModel(self):
return False
def featureCoefficients(self):
return True
@staticmethod
def generateParser(parser):
classifier_group = ClassifierConfiguration.generateParser(parser)
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(
args, experiment)
return params
ClassifierConfFactory.getFactory().registerClass('SvcConfiguration',
SvcConfiguration)
help=help_message)
parser.add_argument('--min-impurity-decrease',
type=float,
default=0,
help=help_message)
parser.add_argument('--presort', type=bool, default=True)
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(
args, experiment)
params['sample_weight'] = args.sample_weight
params['loss'] = args.loss
params['learning_rate'] = args.learning_rate
params['n_estimators'] = args.n_estimators
params['max_features'] = args.max_features
params['criterion'] = args.criterion
params['max_depth'] = args.max_depth
params['min_samples_split'] = args.min_samples_split
params['subsample'] = args.subsample
params['min_samples_leaf'] = args.min_samples_leaf
params['min_weight_fraction_leaf'] = args.min_weight_fraction_leaf
params['max_leaf_nodes'] = args.max_leaf_nodes
params['min_impurity_decrease'] = args.min_impurity_decrease
params['presort'] = args.presort
return params
ClassifierConfFactory.getFactory().registerClass(
'GradientBoostingConfiguration', GradientBoostingConfiguration)
conf['penalty'] = self.penalty.toJson()
return conf
def probabilistModel(self):
return True
def semiSupervisedModel(self):
return False
def featureCoefficients(self):
return not (self.families_supervision)
@staticmethod
def generateParser(parser):
ClassifierConfiguration.generateParser(parser)
parser.add_argument('--optim-algo',
choices=['sag', 'liblinear'],
default='liblinear',
help='sag is recommended for large datasets.')
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(
args, experiment)
params['optim_algo'] = args.optim_algo
return params
ClassifierConfFactory.getFactory().registerClass(
'LogisticRegressionConfiguration', LogisticRegressionConfiguration)
def setBestValues(self, grid_search):
return
def getBestValues(self):
return None
@staticmethod
def fromJson(obj, exp):
conf = LabelPropagationConfiguration(obj['num_folds'],
obj['families_supervision'])
ClassifierConfiguration.setTestConfiguration(conf, obj, exp)
return conf
def toJson(self):
conf = ClassifierConfiguration.toJson(self)
conf['__type__'] = 'LabelPropagationConfiguration'
return conf
def probabilistModel(self):
return True
def semiSupervisedModel(self):
return True
def featureCoefficients(self):
return False
ClassifierConfFactory.getFactory().registerClass(
'LabelPropagationConfiguration', LabelPropagationConfiguration)
type=int,
default=None,
help=help_message)
parser.add_argument('--max-leaf_nodes',
type=int,
default=None,
help=help_message)
parser.add_argument('--min-impurity-decrease',
type=float,
default=0,
help=help_message)
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(
args, experiment)
params['criterion'] = args.criterion
params['splitter'] = args.splitter
params['max_depth'] = args.max_depth
params['min_samples_split'] = args.min_samples_split
params['min_samples_leaf'] = args.min_samples_leaf
params['min_weight_fraction_leaf'] = args.min_weight_fraction_leaf
params['max_features'] = args.max_features
params['max_leaf_nodes'] = args.max_leaf_nodes
params['min_impurity_decrease'] = args.min_impurity_decrease
return params
ClassifierConfFactory.getFactory().registerClass('DecisionTreeConfiguration',
DecisionTreeConfiguration)
type=int,
default=None,
help=help_message)
parser.add_argument('--min-impurity-decrease',
type=float,
default=0,
help=help_message)
parser.add_argument('--bootstrap', type=bool, default=True)
parser.add_argument('--oob-score', type=bool, default=False)
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(
args, experiment)
params['n_estimators'] = args.n_estimators
params['criterion'] = args.criterion
params['max_features'] = args.max_features
params['max_depth'] = args.max_depth
params['min_samples_split'] = args.min_samples_split
params['min_samples_leaf'] = args.min_samples_leaf
params['min_weight_fraction_leaf'] = args.min_weight_fraction_leaf
params['max_leaf_nodes'] = args.max_leaf_nodes
params['min_impurity_decrease'] = args.min_impurity_decrease
params['bootstrap'] = args.bootstrap
params['oob_score'] = args.oob_score
return params
ClassifierConfFactory.getFactory().registerClass('RandomForestConfiguration',
RandomForestConfiguration)
conf = GaussianNaiveBayesConfiguration(obj['num_folds'], obj['sample_weight'],
obj['families_supervision'], test_conf)
ClassifierConfiguration.setTestConfiguration(conf, obj, exp)
return conf
def toJson(self):
conf = ClassifierConfiguration.toJson(self)
conf['__type__'] = 'GaussianNaiveBayesConfiguration'
return conf
def probabilistModel(self):
return True
def semiSupervisedModel(self):
return False
def featureCoefficients(self):
return False
@staticmethod
def generateParser(parser):
classifier_group = ClassifierConfiguration.generateParser(parser)
@staticmethod
def generateParamsFromArgs(args, experiment):
params = ClassifierConfiguration.generateParamsFromArgs(args, experiment)
return params
ClassifierConfFactory.getFactory().registerClass('GaussianNaiveBayesConfiguration',
GaussianNaiveBayesConfiguration)