def filter(cls, instance_name, **kwargs):
if 'dbcache' not in this_thread.misc:
this_thread.misc['dbcache'] = {}
listobj = DAList(instance_name, object_type=cls, auto_gather=False)
filters = []
for key, val in kwargs.items():
if not hasattr(cls._model, key):
raise Exception("filter: class " + cls.__name__ +
" does not have column " + key)
filters.append(getattr(cls._model, key) == val)
for db_entry in list(
cls._session.query(cls._model).filter(*filters).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 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 unclassified_entries(self, key=None):
self._initialize()
results = DAList()._set_instance_name_for_method()
results.gathered = True
if key is None:
query = db.session.execute(
select(MachineLearning).filter_by(
group_id=self.group_id,
active=False).order_by(MachineLearning.id)).scalars()
else:
query = db.session.execute(
select(MachineLearning).filter_by(
group_id=self.group_id, key=key,
active=False).order_by(MachineLearning.id)).scalars()
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')),
create_time=entry.create_time,
key=entry.key,
info=fix_pickle_obj(
codecs.decode(bytearray(entry.info, encoding='utf-8'),
'base64'))
if entry.info is not None else None)
return results
def unclassified_entries(self, key=None):
self._initialize()
results = DAList()._set_instance_name_for_method()
results.gathered = True
if key is None:
query = MachineLearning.query.filter_by(group_id=self.group_id, active=False).order_by(MachineLearning.id).all()
else:
query = MachineLearning.query.filter_by(group_id=self.group_id, key=key, active=False).order_by(MachineLearning.id).all()
for entry in query:
results.appendObject(MachineLearningEntry, ml=self, id=entry.id, independent=pickle.loads(codecs.decode(entry.independent, 'base64')), create_time=entry.create_time, key=entry.key)
return results
def unclassified_entries(self, key=None):
self._initialize()
results = DAList()._set_instance_name_for_method()
results.gathered = True
if key is None:
query = MachineLearning.query.filter_by(group_id=self.group_id, active=False).order_by(MachineLearning.id).all()
else:
query = MachineLearning.query.filter_by(group_id=self.group_id, key=key, active=False).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')), create_time=entry.create_time, key=entry.key, info=fix_pickle_obj(codecs.decode(bytearray(entry.info, encoding='utf-8'), 'base64')) if entry.info is not None else None)
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=pickle.loads(codecs.decode(entry.independent, 'base64')), dependent=pickle.loads(codecs.decode(entry.dependent, 'base64')), info=pickle.loads(codecs.decode(entry.info, 'base64')) if entry.info is not None else None, create_time=entry.create_time, key=entry.key)
return results