def getOutputDirectories(self, al_dir, iteration_dir):
monitoring_dir = path.join(iteration_dir, 'labels_monitoring')
dir_tools.createDirectory(monitoring_dir)
evolution_dir = path.join(al_dir, 'labels_monitoring')
if self.monitoring.iteration_number == 1:
dir_tools.createDirectory(evolution_dir)
return monitoring_dir, evolution_dir
def display(self, directory):
testing_dir = path.join(directory, self.monitoring_type)
dir_tools.createDirectory(testing_dir)
self.finalComputations()
self.predictions_monitoring.display(testing_dir)
if self.has_ground_truth:
self.performance_monitoring.display(testing_dir)
def display(self, directory):
training_dir = path.join(directory, self.monitoring_type)
dir_tools.createDirectory(training_dir)
self.finalComputations()
self.performance_monitoring.display(training_dir)
if not self.conf.families_supervision:
self.predictions_monitoring.display(training_dir)
self.coefficients.display(training_dir)
def export(self, output_dir):
output_dir = path.join(output_dir, str(self.feature_id))
dir_tools.createDirectory(output_dir)
if self.feature_type == FeatureType.binary:
with open(path.join(output_dir, 'binary_histogram.json'), 'w') as f:
self.barplot.export_json(f)
elif self.feature_type == FeatureType.numeric:
self.boxplot.display(path.join(output_dir, 'boxplot.png'))
with open(path.join(output_dir, 'histogram.json'), 'w') as f:
self.barplot.export_json(f)
self.density.display(path.join(output_dir, 'density.png'))
def export(self, output_dir):
output_dir = path.join(output_dir, str(self.feature_id))
dir_tools.createDirectory(output_dir)
## TODO: remove when numpy issue #8627 is solved.
if self.barplot is None:
return
if self.feature_type == FeatureType.binary:
with open(path.join(output_dir, 'binary_histogram.json'),
'w') as f:
self.barplot.export_json(f)
elif self.feature_type == FeatureType.numeric:
self.boxplot.display(path.join(output_dir, 'boxplot.png'))
with open(path.join(output_dir, 'histogram.json'), 'w') as f:
self.barplot.export_json(f)
self.density.display(path.join(output_dir, 'density.png'))
def create_dataset(secuml_conf, project, dataset):
dataset_dir = path.join(secuml_conf.input_data_dir, project, dataset)
dir_tools.createDirectory(dataset_dir)
features_dir = path.join(dataset_dir, 'features')
dir_tools.createDirectory(features_dir)
annotations_dir = path.join(dataset_dir, 'annotations')
dir_tools.createDirectory(annotations_dir)
return dataset_dir, features_dir, annotations_dir
def setOutputDirectory(self):
self.output_directory = path.join(self.monitoring.iteration_dir,
'clustering_evaluation')
dir_tools.createDirectory(self.output_directory)
def export(self):
experiment_dir = self.output_dir()
dir_tools.createDirectory(experiment_dir)
conf_filename = path.join(experiment_dir, 'conf.json')
with open(conf_filename, 'w') as f:
json.dump(self.to_json(), f, indent=2)
def dumpModel(self):
# check added for Sssvdd that has no dump model function
if self.classifier.pipeline is not None:
model_dir = path.join(self.output_dir, 'model')
dir_tools.createDirectory(model_dir)
self.classifier.dumpModel(path.join(model_dir, 'model.out'))
def exportAlerts(self):
if self.exp.alerts is None:
return
alerts_directory = path.join(self.output_dir, 'alerts')
dir_tools.createDirectory(alerts_directory)
self.exp.alerts.export(alerts_directory)
def initComputations(self):
Iteration.initComputations(self)
dir_tools.createDirectory(self.iteration_dir)
self.monitoring.exportStartMonitoring(self.al_dir, self.iteration_dir)
def getEvolutionDir(self, al_dir):
evolution_dir = path.join(al_dir, 'models_performance')
if self.monitoring.iteration_number == 1:
dir_tools.createDirectory(evolution_dir)
return evolution_dir
def getMonitoringDir(self, iteration_dir):
monitoring_dir = path.join(iteration_dir, 'models_performance')
dir_tools.createDirectory(monitoring_dir)
return monitoring_dir