def all(cls, instance_name=None): if 'dbcache' not in this_thread.misc: this_thread.misc['dbcache'] = {} if instance_name: listobj = DAList(instance_name, object_type=cls) else: listobj = DAList(object_type=cls) listobj.set_random_instance_name() for db_entry in list( cls._session.query(cls._model).order_by(cls._model.id).all()): if cls._model.__name__ in this_thread.misc[ 'dbcache'] and db_entry.id in this_thread.misc['dbcache'][ cls._model.__name__]: listobj.append(this_thread.misc['dbcache'][cls._model.__name__] [db_entry.id]) else: obj = listobj.appendObject() obj.id = db_entry.id db_values = {} for column in cls._model.__dict__.keys(): if column == 'id' or column.startswith('_'): continue db_values[column] = getattr(db_entry, column) if db_values[column] is not None: obj.db_set(column, db_values[column]) obj._orig = db_values obj.db_cache() listobj.gathered = True return listobj
def classified_entries(self, key=None): self._initialize() results = DAList() results.gathered = True results.set_random_instance_name() if key is None: query = MachineLearning.query.filter_by( group_id=self.group_id, active=True).order_by(MachineLearning.id).all() else: query = MachineLearning.query.filter_by( group_id=self.group_id, active=True, key=key).order_by(MachineLearning.id).all() for entry in query: results.appendObject( MachineLearningEntry, ml=self, id=entry.id, independent=fix_pickle_obj( codecs.decode( bytearray(entry.independent, encoding='utf-8'), 'base64')), dependent=fix_pickle_obj( codecs.decode(bytearray(entry.dependent, encoding='utf-8'), 'base64')), info=fix_pickle_obj( codecs.decode(bytearray(entry.info, encoding='utf-8'), 'base64')) if entry.info is not None else None, create_time=entry.create_time, key=entry.key) return results
def classified_entries(self, key=None): self._initialize() results = DAList() results.gathered = True results.set_random_instance_name() if key is None: query = MachineLearning.query.filter_by(group_id=self.group_id, active=True).order_by(MachineLearning.id).all() else: query = MachineLearning.query.filter_by(group_id=self.group_id, active=True, key=key).order_by(MachineLearning.id).all() for entry in query: results.appendObject(MachineLearningEntry, ml=self, id=entry.id, independent=fix_pickle_obj(codecs.decode(bytearray(entry.independent, encoding='utf-8'), 'base64')), dependent=fix_pickle_obj(codecs.decode(bytearray(entry.dependent, encoding='utf-8'), 'base64')), info=fix_pickle_obj(codecs.decode(bytearray(entry.info, encoding='utf-8'), 'base64')) if entry.info is not None else None, create_time=entry.create_time, key=entry.key) return results