def run(self, instances=None, cv_monitoring=False): Experiment.run(self) datasets = self._gen_datasets(instances) if self.test_conf.method in ['cv', 'temporal_cv', 'sliding_window']: self._run_cv(datasets, cv_monitoring) else: self._run_one_fold(datasets, cv_monitoring)
def run(self, instances=None, drop_annotated_instances=False, quick=False): Experiment.run(self) instances = self.get_instances() core_conf = self.exp_conf.core_conf clustering = core_conf.algo(instances, core_conf) clustering.fit() clustering.generate(drop_annotated_instances=drop_annotated_instances) clustering.export(self.output_dir(), quick=quick)
def run(self, train_instances, cv_monitoring=False): Experiment.run(self) self._train(train_instances) if cv_monitoring: self._cv_monitoring(train_instances) else: self.cv_monitoring = None self._export()
def set_clusters(self, instances, assigned_clusters, centroids, drop_annotated_instances, cluster_labels): Experiment.run(self) clustering = Clusters(instances, assigned_clusters) clustering.generate(centroids, drop_annotated_instances=drop_annotated_instances, cluster_labels=cluster_labels) clustering.export(self.output_dir(), drop_annotated_instances=drop_annotated_instances)
def run(self): Experiment.run(self) instances = self.get_instances() with_density = instances.num_instances() < 150000 if not with_density: self.exp_conf.logger.warning('There are more than 150.000, so ' 'the density plots are not ' 'displayed. ') stats = FeaturesAnalysis(instances, self.exp_conf.multiclass, self.exp_conf.logger, with_density=with_density) stats.gen_plots(self.output_dir()) stats.gen_scoring(self.output_dir())
def run(self, instances=None, export=True): Experiment.run(self) instances = self.get_instances() core_conf = self.exp_conf.core_conf dimension_reduction = core_conf.algo(core_conf) # Fit dimension_reduction.fit(instances) if export: dimension_reduction.export_fit(self.output_dir(), instances) # Transformation projected_instances = dimension_reduction.transform(instances) if export: dimension_reduction.export_transform(self.output_dir(), instances, projected_instances) return projected_instances
def run(self): Experiment.run(self) datasets = Datasets(self.get_instances()) active_learning = ActiveLearning(self, datasets) if not self.exp_conf.core_conf.auto: from secuml.exp.celery_app.app import secumlworker from secuml.exp.active_learning.celery_tasks import IterationTask options = {} # bind iterations object to IterationTask class active_learning.run_next_iter(output_dir=self.output_dir()) IterationTask.iteration_object = active_learning # Start worker secumlworker.enable_config_fromcmdline = False secumlworker.run(**options) else: active_learning.run_iterations(output_dir=self.output_dir())
def run(self): Experiment.run(self) stats = FeaturesAnalysis(self.get_instances()) stats.compute() stats.export(self.output_dir())
def run(self, classifier, test_instances): Experiment.run(self) self.test_instances = self.get_instances(test_instances) self.classifier = classifier self._test() self._export()
def run(self, train_instances, cv_monitoring=False): Experiment.run(self) self._train(train_instances) if cv_monitoring: self._cv_monitoring(train_instances) self.monitoring.display(self.output_dir())
def run(self, test_instances): Experiment.run(self) self.test_instances = self.get_instances(test_instances) self._test() self._export()