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()