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')