def execute(self):
name = 'AL%d-Iter%d-main' % (self.exp.exp_id, self.iteration.iter_num)
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,
self.model_conf,
None,
name=name,
parent=self.exp.exp_id)
self.model_exp = DiademExp(exp_conf, session=self.exp.session)
classifier_type = get_classifier_type(
self.model_conf.classifier_conf.__class__)
cv_monitoring = classifier_type == ClassifierType.supervised
prev_classifier = None
prev_iter = self.iteration.prev_iter
if prev_iter is not None:
prev_classifier = prev_iter.update_model.classifier
self.model_exp.run(instances=self.iteration.datasets.instances,
cv_monitoring=cv_monitoring,
init_classifier=prev_classifier)
self._set_exec_time()
self.classifier = self.model_exp.get_train_exp().classifier
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 _run_logistic_regression(self):
name = '-'.join([
'AL%d' % (self.exp.exp_id),
'Iter%d' % (self.iteration.iter_num), 'all', 'LogisticRegression'
])
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,
self.exp.exp_conf.core_conf.multiclass_model,
None,
name=name,
parent=self.exp.exp_id)
model_exp = DiademExp(exp_conf, session=self.exp.session)
model_exp.run(instances=self.iteration.datasets.instances,
cv_monitoring=False)
train_exp = model_exp.get_train_exp()
test_exp = model_exp.get_detection_exp('test')
self.lr_predicted_proba = test_exp.predictions.all_probas
self.lr_predicted_labels = test_exp.predictions.values
self.lr_class_labels = train_exp.classifier.class_labels
self.lr_time = train_exp.monitoring.exec_times.total()
self.lr_time += test_exp.monitoring.exec_time.predictions
def _create_clustering_exp(self, core_clustering_conf):
exp_conf = self.test_exp.exp_conf
features_conf = FeaturesConf(
exp_conf.features_conf.input_features,
exp_conf.features_conf.sparse,
exp_conf.features_conf.logger,
filter_in_f=exp_conf.features_conf.filter_in_f,
filter_out_f=exp_conf.features_conf.filter_out_f)
conf = ClusteringConf(exp_conf.secuml_conf,
exp_conf.dataset_conf,
features_conf,
exp_conf.annotations_conf,
core_clustering_conf,
name='Alerts_%i' % exp_conf.exp_id,
parent=exp_conf.exp_id)
return AlertsClusteringExp(conf,
self.test_exp.exp_conf.parent,
create=True,
session=self.test_exp.session)
def _get_multiclass_conf(self):
conf = self.rcd_conf.classification_conf
name = '-'.join(['AL%d' % self.exp.exp_id,
'Iter%d' % self.iteration.iter_num,
self.label,
'analysis'])
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,
conf, None, name=name, parent=self.exp.exp_id)
self.multiclass_exp = DiademExp(exp_conf, session=self.exp.session)
return conf
def _create_clustering_exp(self):
core_conf = CoreClusteringConf(self.exp.exp_conf.logger,
self.categories.num_categories)
name = '-'.join(['AL%d' % self.exp.exp_id,
'Iter%d' % self.iteration.iter_num,
self.label,
'clustering'])
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 = ClusteringConf(self.exp.exp_conf.secuml_conf,
self.exp.exp_conf.dataset_conf,
features_conf,
self.exp.exp_conf.annotations_conf,
core_conf, name=name, parent=self.exp.exp_id)
clustering_exp = ClusteringExperiment(exp_conf,
session=self.exp.session)
return clustering_exp
def create_exp(self):
Experiment.create_exp(self)
# create projection experiment
self.projection_exp = None
if self.exp_conf.core_conf is None:
return
projection_core_conf = self.exp_conf.core_conf.projection_conf
if projection_core_conf is not None:
features_conf = FeaturesConf(
self.exp_conf.features_conf.input_features,
self.exp_conf.secuml_conf.logger)
projection_conf = ProjectionConf(self.exp_conf.secuml_conf,
self.exp_conf.dataset_conf,
features_conf,
self.exp_conf.annotations_conf,
projection_core_conf,
name='-'.join(
[self.exp_conf.name, 'proj']),
parent=self.exp_id)
self.projection_exp = ProjectionExperiment(projection_conf,
session=self.session)