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 _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 _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_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 _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 _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(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)