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
class RcdQueries(CoreRcdQueries):
def __init__(self,
iteration,
label,
proba_min=None,
proba_max=None,
input_checking=True):
CoreRcdQueries.__init__(self,
iteration,
label,
proba_min,
proba_max,
input_checking=input_checking)
self.multiclass_exp = None
self.exp = iteration.exp
def generate_query(self,
instance_id,
predicted_proba,
suggested_label,
suggested_family,
confidence=None):
return Query(instance_id,
predicted_proba,
suggested_label,
suggested_family,
confidence=confidence)
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,
None,
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 _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'
])
exp_conf = ClusteringConf(self.exp.exp_conf.secuml_conf,
self.exp.exp_conf.dataset_conf,
self.exp.exp_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)
clustering_exp.create_exp()
return clustering_exp
def _gen_clustering_visu(self):
if self.families_analysis:
self.clustering_exp = self._create_clustering_exp()
clustering = Clusters(self.categories.instances,
self.categories.assigned_categories)
clustering.generate(None, None)
clustering.export(self.clustering_exp.output_dir())
else:
self.clustering_exp = None
def _set_categories(self, all_instances, assigned_categories,
predicted_proba):
self.categories = Categories(self.multiclass_exp, self.iteration,
all_instances, assigned_categories,
predicted_proba, self.label,
self.multiclass_model.class_labels)