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 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 _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 _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 execute(self):
name = 'AL%d-Iter%d-main' % (self.exp.exp_id, self.iteration.iter_num)
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,
self.model_conf,
name=name,
parent=self.exp.exp_id)
self.model_exp = DiademExp(exp_conf, session=self.exp.session)
self.model_exp.run(instances=self.iteration.datasets.instances,
cv_monitoring=True)
self._set_exec_time()
self.classifier = self.model_exp.get_train_exp().classifier
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'
])
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,
conf,
name=name,
parent=self.exp.exp_id)
self.multiclass_exp = DiademExp(exp_conf, session=self.exp.session)
self.multiclass_exp.create_exp()
return conf
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