def setOutputDirectory(self, output_dir):
self.al_dir = output_dir
self.iteration_dir = None
if self.al_dir is not None:
self.iteration_dir = self.al_dir
self.iteration_dir += str(self.iteration_number) + '/'
dir_tools.createDirectory(self.iteration_dir)
def __init__(self, instances, feature, output_directory):
self.feature = feature
self.output_directory = output_directory + self.feature + '/'
dir_tools.createDirectory(self.output_directory)
self.has_true_labels = instances.hasTrueLabels()
self.generatePlotDatasets(instances)
self.setFeatureType()
def export(self):
experiment_dir = dir_tools.getExperimentOutputDirectory(self)
dir_tools.createDirectory(experiment_dir)
conf_filename = experiment_dir + 'conf.json'
with open(conf_filename, 'w') as f:
json.dump(self.toJson(), f,
indent = 2)
def createOutputDirectories(self, monitoring):
self.output_directory = monitoring.iteration_dir
self.output_directory += 'families_monitoring/'
dir_tools.createDirectory(self.output_directory)
if monitoring.iteration_number == 1:
output_directory = monitoring.AL_directory
output_directory += 'families_monitoring/'
dir_tools.createDirectory(output_directory)
def createOutputDirectories(self):
self.output_directory = self.monitoring.iteration_dir
self.output_directory += 'labels_monitoring/'
dir_tools.createDirectory(self.output_directory)
if self.monitoring.iteration_number == 1:
output_directory = self.monitoring.AL_directory
output_directory += 'labels_monitoring/'
dir_tools.createDirectory(output_directory)
def initializeMonitoring(self):
if self.previous_iteration is not None:
self.previous_iteration.finalComputations()
dir_tools.createDirectory(self.output_directory)
self.monitoring = Monitoring(
self.datasets, self.experiment, self,
self.experiment.validation_conf is not None)
self.monitoring.generateStartMonitoring()
def createOutputDirectories(self, monitoring):
output_directory = monitoring.iteration_dir
output_directory += 'models_performance/'
dir_tools.createDirectory(output_directory)
if monitoring.iteration_number == 1:
output_directory = monitoring.AL_directory
output_directory += 'models_performance/'
dir_tools.createDirectory(output_directory)
def createOutputDirectories(self):
self.output_directory = self.monitoring.iteration_dir
self.output_directory += 'suggestions_accuracy/'
dir_tools.createDirectory(self.output_directory)
if self.monitoring.iteration_number == 1:
output_directory = self.monitoring.AL_directory
output_directory += 'suggestions_accuracy/'
dir_tools.createDirectory(output_directory)
def getOutputDirectories(self):
monitoring_dir = self.monitoring.iteration_dir + 'families_monitoring/'
dir_tools.createDirectory(monitoring_dir)
evolution_dir = self.monitoring.al_dir + 'families_monitoring/'
if self.monitoring.iteration_number == 1:
dir_tools.createDirectory(evolution_dir)
return monitoring_dir, evolution_dir
def getOutputDirectories(self):
monitoring_dir = self.monitoring.iteration_dir + 'suggestions_accuracy/'
dir_tools.createDirectory(monitoring_dir)
evolution_dir = self.monitoring.al_dir + 'suggestions_accuracy/'
if self.monitoring.iteration_number == 1:
dir_tools.createDirectory(evolution_dir)
return monitoring_dir, evolution_dir
def display(self, directory):
testing_dir = directory + self.monitoring_type + '/'
dir_tools.createDirectory(testing_dir)
self.finalComputations()
self.predictions_monitoring.display(testing_dir)
if self.has_true_labels:
self.performance_monitoring.display(testing_dir)
if self.families_monitoring is not None:
families_dir = testing_dir + 'families/'
dir_tools.createDirectory(families_dir)
self.families_monitoring.display(families_dir)
def getOutputDirectories(self):
monitoring_dir = self.monitoring.iteration_dir
monitoring_dir += 'models_performance/'
dir_tools.createDirectory(monitoring_dir)
evolution_dir = self.monitoring.al_dir
evolution_dir += 'models_performance/'
if self.monitoring.iteration_number == 1:
dir_tools.createDirectory(evolution_dir)
return monitoring_dir, evolution_dir
def display(self, directory):
training_dir = 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)
if self.families_monitoring is not None:
families_dir = training_dir + 'families/'
dir_tools.createDirectory(families_dir)
self.families_monitoring.display(families_dir)
def createDirectories(self):
dir_tools.createDirectory(
dir_tools.getDatasetOutputDirectory(self.project, self.dataset))
def setOutputDirectory(self):
self.output_directory = self.monitoring.iteration_dir
self.output_directory += 'clustering_evaluation/'
dir_tools.createDirectory(self.output_directory)
def dumpModel(self):
model_dir = self.output_directory + 'model/'
dir_tools.createDirectory(model_dir)
joblib.dump(self.pipeline, model_dir + 'model.out')
