class UpdateModel(CoreUpdateModel):
def __init__(self, iteration):
CoreUpdateModel.__init__(self, iteration)
self.exp = self.iteration.exp
self.model_exp = None
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 _set_exec_time(self):
training = self.model_exp.get_train_exp().monitoring
training_detect = self.model_exp.get_detection_exp('train').monitoring
detection = self.model_exp.get_detection_exp('test').monitoring
self.exec_time = sum(
[m.exec_time for m in [training, training_detect, detection]])
def monitoring(self, al_dir, iteration_dir):
with_validation = self.iteration.conf.validation_conf is not None
self.monitoring = ModelPerfEvolution(self.iteration.iter_num,
self.model_exp, with_validation)
self.monitoring.generate()
self.monitoring.export(iteration_dir, al_dir)
class UpdateModel(CoreUpdateModel):
def __init__(self, iteration):
CoreUpdateModel.__init__(self, iteration)
self.exp = self.iteration.exp
self.model_exp = None
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 _set_exec_time(self):
training = self.model_exp.get_train_exp().monitoring
training_detect = self.model_exp.get_detection_exp('train').monitoring
detection = self.model_exp.get_detection_exp('test').monitoring
self.exec_time = training.exec_times.total()
self.exec_time += sum(
[m.exec_time.predictions for m in [training_detect, detection]])
def monitoring(self, al_dir, iteration_dir):
with_validation = self.iteration.conf.validation_conf is not None
self.monitoring = ModelPerfEvolution(self.iteration.iter_num,
self.model_exp, with_validation)
self.monitoring.generate()
self.monitoring.export(iteration_dir, al_dir)
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