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 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
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 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 get_contacts(self, upn, default_address='home'):
""" Return a list of contacts from the given user's Universal Principal Name (i.e., [email protected] for most organizations, or [email protected]).
Does not paginate--will return the first 100 contacts only for now. You can choose whether to default to 'home' or 'business' address and phone."""
contacts_url = "https://graph.microsoft.com/v1.0/users/" + upn + "/contacts"
res = self.get_request(contacts_url)
people = DAList(object_type=Individual,
auto_gather=False, gathered=True)
for p_res in res.get('value', []):
person = people.appendObject()
person.name.first = p_res.get('givenName', '')
person.name.last = p_res.get('surname', '')
if p_res.get('middleName'):
person.name.middle = p_res.get('middleName')
person.jobTitle = p_res.get('jobTitle')
person.title = p_res.get('title')
person.business_phones = p_res.get('businessPhones', [])
person.home_phones = p_res.get('homePhones', [])
person.mobile_number = p_res.get('mobilePhone')
person.initializeAttribute('home_address', Address)
person.home_address.address = p_res.get(
'homeAddress', {}).get('street')
person.home_address.city = p_res.get('homeAddress', {}).get('city')
person.home_address.state = p_res.get(
'homeAddress', {}).get('state')
person.home_address.zip = p_res.get(
'homeAddress', {}).get('postalCode')
# if not p_res.get('homeAddress',{}).get('countryOrRegion') is None:
# person.home_address.country = p_res.get('homeAddress',{}).get('countryOrRegion')
person.initializeAttribute('business_address', Address)
person.business_address.address = p_res.get(
'businessAddress', {}).get('street')
person.business_address.city = p_res.get(
'businessAddress', {}).get('city')
person.business_address.state = p_res.get(
'businessAddress', {}).get('state')
person.business_address.zip = p_res.get(
'businessAddress', {}).get('postalCode')
# if not p_res.get('businessAddress',{}).get('countryOrRegion') is None:
# person.business_address.country = p_res.get('businessAddress',{}).get('countryOrRegion')
person.emails = p_res.get('emailAddresses', [])
if p_res.get('emailAddresses'):
person.email = next(iter(person.emails), []).get(
'address', None) # take the first email
# Try to respect the address kind the user wants, but if not present, use the address we have (which might be null)
# Information is often put in the business phone/address fields if the contact only has one address/phone
if default_address == 'home':
if p_res.get('homeAddress'):
person.address = person.home_address
else:
person.address = person.business_address
else:
if p_res.get('businessAddress'):
person.address = person.business_address
else:
person.address = person.home_address
if default_address == 'home':
if p_res.get('homePhones'):
# just take the first home phone in the list
person.phone_number = next(iter(person.home_phones), '')
else:
person.phone_number = next(
iter(person.business_phones), '')
else:
if p_res.get('businessPhones'):
# just take the first business phone
person.phone_number = next(
iter(person.business_phones), '')
else:
person.phone_number = next(iter(person.home_phones), '')
return people