def setUp(self):
self.work_state_1 = WorkerState(TEST_WORKER_ID_1, 10)
self.work_state_2 = WorkerState(TEST_WORKER_ID_2)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args([])
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(opt=self.opt, mturk_agent_ids=mturk_agent_ids)
self.worker_manager = WorkerManager(self.mturk_manager, self.opt)
def setUp(self):
self.agent_state1 = AssignState()
self.agent_state2 = AssignState(status=AssignState.STATUS_IN_TASK)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args(print_args=False)
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(opt=self.opt,
mturk_agent_ids=mturk_agent_ids)
self.worker_manager = self.mturk_manager.worker_manager
def setUp(self):
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args(print_args=False)
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(opt=self.opt.copy(),
mturk_agent_ids=mturk_agent_ids)
self.worker_manager = self.mturk_manager.worker_manager
self.turk_agent = MTurkAgent(self.opt.copy(), self.mturk_manager,
TEST_HIT_ID_1, TEST_ASSIGNMENT_ID_1,
TEST_WORKER_ID_1)
def __init__(self, port=DEFAULT_PORT, db_file=DEFAULT_DB_FILE,
is_sandbox=False):
self.state = {'is_sandbox': is_sandbox}
self.subs = {}
self.sources = {}
self.port = port
self.data_handler = MTurkDataHandler(file_name=db_file)
self.mturk_manager = MTurkManager.make_taskless_instance(is_sandbox)
self.mturk_manager.db_logger = self.data_handler
# TODO load some state from DB
handlers = [
(r"/app/(.*)", AppHandler, {'app': self}),
(r"/tasks", TaskListHandler, {'app': self}),
(r"/workers", WorkerListHandler, {'app': self}),
(r"/runs/(.*)", RunHandler, {'app': self}),
(r"/workers/(.*)", WorkerHandler, {'app': self}),
(r"/assignments/(.*)", AssignmentHandler, {'app': self}),
(r"/approve/(.*)", ApprovalHandler, {'app': self}),
(r"/reject/(.*)", RejectionHandler, {'app': self}),
(r"/reverse_rejection/(.*)", ReverseHandler, {'app': self}),
(r"/block/(.*)", BlockHandler, {'app': self}),
(r"/bonus/(.*)", BonusHandler, {'app': self}),
(r"/error/(.*)", ErrorHandler, {'app': self}),
(r"/socket", SocketHandler, {'app': self}),
(r"/", RedirectHandler),
]
super(Application, self).__init__(handlers, **tornado_settings)
def setUp(self):
disconnect_path = os.path.join(parent_dir, 'disconnect-test.pickle')
if os.path.exists(disconnect_path):
os.remove(disconnect_path)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args([])
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(
opt=self.opt.copy(), mturk_agent_ids=mturk_agent_ids
)
self.worker_manager = self.mturk_manager.worker_manager
self.worker_state_1 = self.worker_manager.worker_alive(TEST_WORKER_ID_1)
self.worker_state_2 = self.worker_manager.worker_alive(TEST_WORKER_ID_2)
self.worker_state_3 = self.worker_manager.worker_alive(TEST_WORKER_ID_3)
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_1_id = 'mturk_agent_1'
mturk_agent_2_id = 'mturk_agent_2'
human_agent_1_id = 'human_1'
mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id]
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = MTurkMultiAgentDialogOnboardWorld(opt=opt,
mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(
onboard_function=run_onboard
) # Set onboard_function to None to skip onboarding
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
global worker_count
worker_count = 0
def get_worker_role(worker):
global worker_count
worker_role = mturk_agent_ids[worker_count % len(mturk_agent_ids)]
worker_count += 1
return worker_role
def run_conversation(mturk_manager, opt, workers):
# Create mturk agents
mturk_agent_1 = workers[0]
mturk_agent_2 = workers[1]
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
world = MTurkMultiAgentDialogWorld(
opt=opt, agents=[human_agent_1, mturk_agent_1, mturk_agent_2])
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.start_task(eligibility_function=check_worker_eligibility,
role_function=get_worker_role,
task_function=run_conversation)
except:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
completed_workers = []
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=[mturk_agent_id]
)
mturk_manager.setup_server()
qual_name = 'ParlAIExcludeQual{}t{}'.format(
random.randint(10000, 99999), random.randint(10000, 99999))
qual_desc = (
'Qualification for a worker not correctly completing the '
'first iteration of a task. Used to filter to different task pools.'
)
qualification_id = \
mturk_utils.find_or_create_qualification(qual_name, qual_desc)
print('Created qualification: ', qualification_id)
def run_onboard(worker):
world = QualificationFlowOnboardWorld(opt, worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
try:
mturk_manager.start_new_run()
agent_qualifications = [{
'QualificationTypeId': qualification_id,
'Comparator': 'DoesNotExist',
'RequiredToPreview': True
}]
mturk_manager.create_hits(qualifications=agent_qualifications)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_agent = workers[0]
world = QualificationFlowSoloWorld(
opt=opt,
mturk_agent=mturk_agent,
qualification_id=qualification_id,
firstTime=(mturk_agent.worker_id not in completed_workers),
)
while not world.episode_done():
world.parley()
completed_workers.append(mturk_agent.worker_id)
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_utils.delete_qualification(qualification_id)
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
# Initialize a SQuAD teacher agent, which we will get context from
module_name = 'parlai.tasks.squad.agents'
class_name = 'DefaultTeacher'
my_module = importlib.import_module(module_name)
task_class = getattr(my_module, class_name)
task_opt = {}
task_opt['datatype'] = 'train'
task_opt['datapath'] = opt['datapath']
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
mturk_manager.setup_server()
def run_onboard(worker):
world = QADataCollectionOnboardWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=None)
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
mturk_manager.ready_to_accept_workers()
def check_workers_eligibility(workers):
return workers
eligibility_function = {
'func': check_workers_eligibility,
'multiple': True,
}
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
task = task_class(task_opt)
mturk_agent = workers[0]
world = QADataCollectionWorld(opt=opt,
task=task,
mturk_agent=mturk_agent)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.start_task(eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""This task consists of one agent, model or MTurk worker, talking to an
MTurk worker to negotiate a deal.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-min_t',
'--min_turns',
default=5,
type=int,
help='minimum number of turns')
argparser.add_argument('-mt',
'--max_turns',
default=10,
type=int,
help='maximal number of chat turns')
argparser.add_argument(
'-mx_rsp_time',
'--max_resp_time',
default=150,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'--ag_shutdown_time',
default=120,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'--persona-type',
default='both',
type=str,
choices=['both', 'self', 'other'],
help='Which personas to load from personachat',
)
opt = argparser.parse_args()
directory_path = os.path.dirname(os.path.abspath(__file__))
opt['task'] = os.path.basename(directory_path)
opt['extract_personas_path'] = os.path.join(opt['datapath'], opt['task'])
opt.update(task_config)
mturk_agent_ids = ['PERSON_1']
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
mturk_manager.setup_server(task_directory_path=directory_path)
personas_generator = PersonasGenerator(opt)
opt['personas_generator'] = personas_generator
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
if not opt['is_sandbox']:
# ADD BLOCKED WORKERS HERE
blocked_worker_list = []
for w in blocked_worker_list:
mturk_manager.block_worker(
w,
'We found that you have unexpected behaviors in our '
'previous HITs. For more questions please email us.',
)
def run_onboard(worker):
pass
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
worker = workers[0]
world = RephrasePersonaWorld(opt, worker)
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
class TestMTurkManagerWorkflows(unittest.TestCase):
"""
Various test cases to replicate a whole mturk workflow.
"""
def setUp(self):
patcher = mock.patch('builtins.input', return_value='y')
self.addCleanup(patcher.stop)
patcher.start()
# Mock functions that hit external APIs and such
self.server_utils = MTurkManagerFile.server_utils
self.mturk_utils = MTurkManagerFile.mturk_utils
self.server_utils.setup_server = mock.MagicMock(
return_value='https://127.0.0.1')
self.server_utils.setup_legacy_server = mock.MagicMock(
return_value='https://127.0.0.1')
self.server_utils.delete_server = mock.MagicMock()
self.mturk_utils.setup_aws_credentials = mock.MagicMock()
self.mturk_utils.calculate_mturk_cost = mock.MagicMock(return_value=1)
self.mturk_utils.check_mturk_balance = mock.MagicMock(
return_value=True)
self.mturk_utils.create_hit_config = mock.MagicMock()
self.mturk_utils.setup_sns_topic = mock.MagicMock(
return_value=TOPIC_ARN)
self.mturk_utils.delete_sns_topic = mock.MagicMock()
self.mturk_utils.delete_qualification = mock.MagicMock()
self.mturk_utils.find_or_create_qualification = mock.MagicMock(
return_value=QUALIFICATION_ID)
self.mturk_utils.find_qualification = mock.MagicMock(
return_value=QUALIFICATION_ID)
self.mturk_utils.give_worker_qualification = mock.MagicMock()
self.mturk_utils.remove_worker_qualification = mock.MagicMock()
self.mturk_utils.create_hit_type = mock.MagicMock(
return_value=HIT_TYPE_ID)
self.mturk_utils.subscribe_to_hits = mock.MagicMock()
self.mturk_utils.create_hit_with_hit_type = mock.MagicMock(
return_value=(MTURK_PAGE_URL, FAKE_HIT_ID, 'MTURK_HIT_DATA'))
self.mturk_utils.get_mturk_client = mock.MagicMock(
return_value=mock.MagicMock())
self.onboarding_agents = {}
self.worlds_agents = {}
# Set up an MTurk Manager and get it ready for accepting workers
self.fake_socket = MockSocket()
time.sleep(0.1)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args()
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 1
self.opt['hit_title'] = 'test_hit_title'
self.opt['hit_description'] = 'test_hit_description'
self.opt['task_description'] = 'test_task_description'
self.opt['hit_keywords'] = 'test_hit_keywords'
self.opt['reward'] = 0.1
self.opt['is_debug'] = True
self.opt['log_level'] = 0
self.opt['num_conversations'] = 1
self.mturk_agent_ids = ['mturk_agent_1', 'mturk_agent_2']
self.mturk_manager = MTurkManager(opt=self.opt,
mturk_agent_ids=self.mturk_agent_ids,
is_test=True)
self.mturk_manager.port = self.fake_socket.port
self.mturk_manager.setup_server()
self.mturk_manager.start_new_run()
self.mturk_manager.ready_to_accept_workers()
self.mturk_manager.set_onboard_function(self.onboard_agent)
self.mturk_manager.create_hits()
def assign_worker_roles(workers):
workers[0].id = 'mturk_agent_1'
workers[1].id = 'mturk_agent_2'
def run_task_wait():
self.mturk_manager.start_task(lambda w: True, assign_worker_roles,
self.run_conversation)
self.task_thread = threading.Thread(target=run_task_wait)
self.task_thread.start()
self.agent_1 = MockAgent(TEST_HIT_ID_1, TEST_ASSIGNMENT_ID_1,
TEST_WORKER_ID_1, TASK_GROUP_ID_1)
self.agent_1_2 = MockAgent(TEST_HIT_ID_1, TEST_ASSIGNMENT_ID_3,
TEST_WORKER_ID_1, TASK_GROUP_ID_1)
self.agent_2 = MockAgent(TEST_HIT_ID_2, TEST_ASSIGNMENT_ID_2,
TEST_WORKER_ID_2, TASK_GROUP_ID_1)
def tearDown(self):
self.agent_1.always_beat = False
self.agent_2.always_beat = False
for key in self.worlds_agents.keys():
self.worlds_agents[key] = True
self.mturk_manager.shutdown()
self.fake_socket.close()
self.task_thread.join()
def onboard_agent(self, worker):
self.onboarding_agents[worker.worker_id] = False
while (worker.worker_id in self.onboarding_agents) and (
self.onboarding_agents[worker.worker_id] is False):
time.sleep(0.05)
return
def run_conversation(self, mturk_manager, opt, workers):
for worker in workers:
self.worlds_agents[worker.worker_id] = False
for worker in workers:
while self.worlds_agents[worker.worker_id] is False:
time.sleep(0.05)
for worker in workers:
worker.shutdown(timeout=-1)
def alive_agent(self, agent):
agent.register_to_socket(self.fake_socket)
agent.wait_for_alive()
agent.send_heartbeat()
def test_successful_convo(self):
manager = self.mturk_manager
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents move to task
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(lambda: agent_2.worker_id in self.worlds_agents, True,
2)
self.assertIn(agent_1.worker_id, self.worlds_agents)
# Complete agents
self.worlds_agents[agent_1.worker_id] = True
self.worlds_agents[agent_2.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_DONE, 2)
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_DONE, 2)
# Assert conversation is complete for manager and agents
assert_equal_by(lambda: manager.completed_conversations, 1, 2)
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 2,
2)
def test_disconnect_end(self):
manager = self.mturk_manager
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents move to task
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(lambda: agent_2.worker_id in self.worlds_agents, True,
2)
self.assertIn(agent_1.worker_id, self.worlds_agents)
# Disconnect agent
agent_2.always_beat = False
assert_equal_by(agent_1_object.get_status,
AssignState.STATUS_PARTNER_DISCONNECT, 3)
assert_equal_by(agent_2_object.get_status,
AssignState.STATUS_DISCONNECT, 3)
self.worlds_agents[agent_1.worker_id] = True
self.worlds_agents[agent_2.worker_id] = True
agent_2.always_beat = True
agent_2.send_alive()
# Assert workers get the correct command
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_INACTIVE_DONE
]),
1,
2,
)
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_INACTIVE_HIT
]),
1,
2,
)
# assert conversation not marked as complete
self.assertEqual(manager.completed_conversations, 0)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 2,
2)
def test_expire_onboarding(self):
manager = self.mturk_manager
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 10)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
manager._expire_onboarding_pool()
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_EXPIRE_HIT
]),
1,
10,
)
self.onboarding_agents[agent_1.worker_id] = True
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_EXPIRED)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]),
1,
10,
)
def test_reconnect_complete(self):
manager = self.mturk_manager
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents move to task
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(lambda: agent_2.worker_id in self.worlds_agents, True,
2)
self.assertIn(agent_1.worker_id, self.worlds_agents)
# Simulate reconnect to task
stored_conv_id = agent_2.conversation_id
stored_agent_id = agent_2.id
agent_2.conversation_id = None
agent_2.id = None
agent_2.send_alive()
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_RESTORE_STATE
]),
1,
4,
)
self.assertEqual(agent_2.id, stored_agent_id)
self.assertEqual(agent_2.conversation_id, stored_conv_id)
# Complete agents
self.worlds_agents[agent_1.worker_id] = True
self.worlds_agents[agent_2.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_DONE, 2)
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_DONE, 2)
# Assert conversation is complete for manager and agents
assert_equal_by(lambda: manager.completed_conversations, 1, 2)
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 2,
2)
def test_attempt_break_unique(self):
manager = self.mturk_manager
unique_worker_qual = 'is_unique_qual'
manager.is_unique = True
manager.opt['unique_qual_name'] = unique_worker_qual
manager.unique_qual_name = unique_worker_qual
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents move to task
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(lambda: agent_2.worker_id in self.worlds_agents, True,
2)
self.assertIn(agent_1.worker_id, self.worlds_agents)
# Complete agents
self.worlds_agents[agent_1.worker_id] = True
self.worlds_agents[agent_2.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_DONE, 2)
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_DONE, 2)
# Assert conversation is complete for manager and agents
assert_equal_by(lambda: manager.completed_conversations, 1, 2)
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 2,
2)
# ensure qualification was 'granted'
self.mturk_utils.find_qualification.assert_called_with(
unique_worker_qual, manager.is_sandbox)
self.mturk_utils.give_worker_qualification.assert_any_call(
agent_1.worker_id, QUALIFICATION_ID, None, manager.is_sandbox)
self.mturk_utils.give_worker_qualification.assert_any_call(
agent_2.worker_id, QUALIFICATION_ID, None, manager.is_sandbox)
# Try to alive with the first agent a second time
agent_1_2 = self.agent_1_2
self.alive_agent(agent_1_2)
assert_equal_by(lambda: agent_1_2.worker_id in self.onboarding_agents,
True, 2)
agent_1_2_object = manager.worker_manager.get_agent_for_assignment(
agent_1_2.assignment_id)
# No worker should be created for a unique task
self.assertIsNone(agent_1_2_object)
assert_equal_by(
lambda: len([
p for p in agent_1_2.message_packet
if p.data['text'] == data_model.COMMAND_EXPIRE_HIT
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 3,
2)
def test_break_multi_convo(self):
manager = self.mturk_manager
manager.opt['allowed_conversations'] = 1
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents move to task
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(lambda: agent_2.worker_id in self.worlds_agents, True,
2)
self.assertIn(agent_1.worker_id, self.worlds_agents)
# Attempt to start a new conversation with duplicate worker 1
agent_1_2 = self.agent_1_2
self.alive_agent(agent_1_2)
assert_equal_by(lambda: agent_1_2.worker_id in self.onboarding_agents,
True, 2)
agent_1_2_object = manager.worker_manager.get_agent_for_assignment(
agent_1_2.assignment_id)
# No worker should be created for a unique task
self.assertIsNone(agent_1_2_object)
assert_equal_by(
lambda: len([
p for p in agent_1_2.message_packet
if p.data['text'] == data_model.COMMAND_EXPIRE_HIT
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 1,
2)
# Complete agents
self.worlds_agents[agent_1.worker_id] = True
self.worlds_agents[agent_2.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_DONE, 2)
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_DONE, 2)
# Assert conversation is complete for manager and agents
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
assert_equal_by(
lambda: len([
p for p in agent_2.message_packet
if p.data['text'] == data_model.COMMAND_SHOW_DONE_BUTTON
]),
1,
2,
)
assert_equal_by(lambda: manager.completed_conversations, 1, 2)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 3,
2)
def test_no_onboard_expire_waiting(self):
manager = self.mturk_manager
manager.set_onboard_function(None)
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
manager._expire_agent_pool()
assert_equal_by(
lambda: len([
p for p in agent_1.message_packet
if p.data['text'] == data_model.COMMAND_EXPIRE_HIT
]),
1,
2,
)
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 1,
2)
def test_return_to_waiting_on_world_start(self):
manager = self.mturk_manager
# Alive first agent
agent_1 = self.agent_1
self.alive_agent(agent_1)
assert_equal_by(lambda: agent_1.worker_id in self.onboarding_agents,
True, 2)
agent_1_object = manager.worker_manager.get_agent_for_assignment(
agent_1.assignment_id)
self.assertFalse(self.onboarding_agents[agent_1.worker_id])
self.assertEqual(agent_1_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_1.worker_id] = True
assert_equal_by(agent_1_object.get_status, AssignState.STATUS_WAITING,
2)
# Make agent_1 no longer respond to change_conversation_requests
def replace_on_msg(packet):
agent_1.message_packet.append(packet)
agent_1.on_msg = replace_on_msg
# Alive second agent
agent_2 = self.agent_2
self.alive_agent(agent_2)
assert_equal_by(lambda: agent_2.worker_id in self.onboarding_agents,
True, 2)
agent_2_object = manager.worker_manager.get_agent_for_assignment(
agent_2.assignment_id)
self.assertFalse(self.onboarding_agents[agent_2.worker_id])
self.assertEqual(agent_2_object.get_status(),
AssignState.STATUS_ONBOARDING)
self.onboarding_agents[agent_2.worker_id] = True
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
2)
# Assert agents attempt to move to task, but then move back to waiting
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_IN_TASK,
2)
assert_equal_by(agent_2_object.get_status, AssignState.STATUS_WAITING,
3)
agent_1.always_beat = False
# Assert no world ever started
self.assertNotIn(agent_2.worker_id, self.worlds_agents)
# Expire everything
manager.shutdown()
# Assert sockets are closed
assert_equal_by(
lambda: len(
[x for x in manager.socket_manager.run.values() if not x]), 2,
2)
def main():
"""
IGC Human Evaluation.
Specify the `--eval-data-path` to load examples for evaluation.
The data in `--eval-data-path` should be formatted as a dictionary
mapping IGC image ids to dicts with the following fields:
{
'questions': list of (<generator_name>, <generated_question>) tuples,
'responses': list of (<generator_name>, <generated_response>) tuples,
'question': question to use when evaluating responses,
'context': context for the image
}
If not data path specified, loads a demo_example specified in worlds.py
Specify `--image-path` for the path to the IGC images, where each example
is saved as <image_id>.jpg
NOTE: You can download the IGC Test Set from
https://www.microsoft.com/en-us/download/details.aspx?id=55324
And you can use the `download_igc_images.py` script to download the images
(please put the IGC_crowd_test.csv file in this directory to use the script)
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-min_t',
'--min_turns',
default=3,
type=int,
help='minimum number of turns')
argparser.add_argument('-mt',
'--max_turns',
default=5,
type=int,
help='maximal number of chat turns')
argparser.add_argument('-mx_rsp_time',
'--max_resp_time',
default=1800,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('-mx_onb_time',
'--max_onboard_time',
type=int,
default=300,
help='time limit for turker'
'in onboarding')
argparser.add_argument('-ni',
'--num_images',
type=int,
default=5,
help='number of images to show \
to turker')
argparser.add_argument('--auto-approve-delay',
type=int,
default=3600 * 24,
help='how long to wait for \
auto approval')
argparser.add_argument('--data-path',
type=str,
default='',
help='where to save data')
argparser.add_argument('--eval-data-path',
type=str,
default='',
help='path to file with candidates to '
'evaluate')
argparser.add_argument('--image-path',
type=str,
default='',
help='path to IGC images')
argparser.add_argument('-rnd',
'--dialog-round',
type=str,
default='questions',
choices=round_choices,
help='which dialog round to show')
opt = argparser.parse_args()
directory_path = os.path.dirname(os.path.abspath(__file__))
opt['task'] = os.path.basename(directory_path)
if 'data_path' not in opt or opt['data_path'] == '':
opt['data_path'] = "{}/data/{}_evals".format(os.getcwd(),
opt['dialog_round'])
opt['task_dir'] = os.getcwd()
if opt['dialog_round'] == 'questions':
opt.update(tc_questions)
else:
opt.update(tc_responses)
mturk_agent_ids = [RATER]
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
example_generator = IGCExampleGenerator(opt)
mturk_manager.setup_server(task_directory_path=directory_path)
try:
mturk_manager.start_new_run()
def run_onboard(worker):
worker.example_generator = example_generator
world = RoleOnboardWorld(opt, worker)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
mturk_manager.create_hits()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for w in workers:
w.id = mturk_agent_ids[0]
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
conv_idx = mturk_manager.conversation_index
world = MTurkIGCEvalWorld(
opt,
agents=agents,
world_tag='conversation t_{}'.format(conv_idx),
)
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
world.review_work()
mturk_manager.start_task(eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""This task consists of an MTurk agent evaluating a wizard model. They
are assigned a topic and asked to chat.
"""
start_time = datetime.datetime.today().strftime('%Y-%m-%d-%H-%M')
argparser = ParlaiParser(False, add_model_args=True)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-mt',
'--max-turns',
default=10,
type=int,
help='maximal number of chat turns')
argparser.add_argument('--max-resp-time',
default=240,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('--max-choice-time',
type=int,
default=300,
help='time limit for turker'
'choosing the topic')
argparser.add_argument('--ag-shutdown-time',
default=120,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('-rt',
'--range-turn',
default='3,5',
help='sample range of number of turns')
argparser.add_argument('--human-eval',
type='bool',
default=False,
help='human vs human eval, no models involved')
argparser.add_argument('--auto-approve-delay',
type=int,
default=3600 * 24 * 1,
help='how long to wait for auto approval')
argparser.add_argument('--only-masters',
type='bool',
default=False,
help='Set to true to use only master turks for '
'this test eval')
argparser.add_argument('--unique-workers',
type='bool',
default=False,
help='Each worker must be unique')
argparser.add_argument('--mturk-log',
type=str,
default='data/mturklogs/{}.log'.format(start_time))
def inject_override(opt, override_dict):
opt['override'] = override_dict
for k, v in override_dict.items():
opt[k] = v
def get_logger(opt):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
fmt = logging.Formatter('%(asctime)s: [ %(message)s ]',
'%m/%d/%Y %I:%M:%S %p')
console = logging.StreamHandler()
console.setFormatter(fmt)
logger.addHandler(console)
if 'mturk_log' in opt:
logfile = logging.FileHandler(opt['mturk_log'], 'a')
logfile.setFormatter(fmt)
logger.addHandler(logfile)
logger.info('COMMAND: %s' % ' '.join(sys.argv))
logger.info('-' * 100)
logger.info('CONFIG:\n%s' % json.dumps(opt, indent=4, sort_keys=True))
return logger
# MODEL CONFIG
# NOTE: please edit this to test your own models
config = {
'model':
'projects:wizard_of_wikipedia:interactive_retrieval',
'retriever_model_file':
'models:wikipedia_full/tfidf_retriever/model',
'responder_model_file':
'models:wizard_of_wikipedia/full_dialogue_retrieval_model/model',
}
argparser.add_model_subargs(config['model']) # add model args to opt
start_opt = argparser.parse_args()
inject_override(start_opt, config)
if not start_opt.get('human_eval'):
bot = create_agent(start_opt)
shared_bot_params = bot.share()
else:
shared_bot_params = None
if not start_opt['human_eval']:
get_logger(bot.opt)
else:
get_logger(start_opt)
if start_opt['human_eval']:
folder_name = 'human_eval-{}'.format(start_time)
else:
folder_name = '{}-{}'.format(start_opt['model'], start_time)
start_opt['task'] = os.path.basename(
os.path.dirname(os.path.abspath(__file__)))
if 'data_path' not in start_opt:
start_opt['data_path'] = os.path.join(os.getcwd(), 'data',
'wizard_eval', folder_name)
start_opt.update(task_config)
if not start_opt.get('human_eval'):
mturk_agent_ids = ['PERSON_1']
else:
mturk_agent_ids = ['PERSON_1', 'PERSON_2']
mturk_manager = MTurkManager(opt=start_opt,
mturk_agent_ids=mturk_agent_ids)
topics_generator = TopicsGenerator(start_opt)
directory_path = os.path.dirname(os.path.abspath(__file__))
mturk_manager.setup_server(task_directory_path=directory_path)
worker_roles = {}
connect_counter = AttrDict(value=0)
try:
mturk_manager.start_new_run()
agent_qualifications = []
if not start_opt['is_sandbox']:
# assign qualifications
if start_opt['only_masters']:
agent_qualifications.append(MASTER_QUALIF)
if start_opt['unique_workers']:
qual_name = 'UniqueChatEval'
qual_desc = (
'Qualification to ensure each worker completes a maximum '
'of one of these chat/eval HITs')
qualification_id = \
mturk_utils.find_or_create_qualification(qual_name, qual_desc,
False)
print('Created qualification: ', qualification_id)
UNIQUE_QUALIF = {
'QualificationTypeId': qualification_id,
'Comparator': 'DoesNotExist',
'RequiredToPreview': True
}
start_opt['unique_qualif_id'] = qualification_id
agent_qualifications.append(UNIQUE_QUALIF)
mturk_manager.create_hits(qualifications=agent_qualifications)
def run_onboard(worker):
if start_opt['human_eval']:
role = mturk_agent_ids[connect_counter.value %
len(mturk_agent_ids)]
connect_counter.value += 1
worker_roles[worker.worker_id] = role
else:
role = 'PERSON_1'
worker.topics_generator = topics_generator
world = TopicChooseWorld(start_opt, worker, role=role)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_single_worker_eligibility(worker):
return True
def check_multiple_workers_eligibility(workers):
valid_workers = {}
for worker in workers:
worker_id = worker.worker_id
if worker_id not in worker_roles:
print('Something went wrong')
continue
role = worker_roles[worker_id]
if role not in valid_workers:
valid_workers[role] = worker
if len(valid_workers) == 2:
break
return valid_workers.values() if len(valid_workers) == 2 else []
if not start_opt['human_eval']:
eligibility_function = {
'func': check_single_worker_eligibility,
'multiple': False,
}
else:
eligibility_function = {
'func': check_multiple_workers_eligibility,
'multiple': True,
}
def assign_worker_roles(workers):
if start_opt['human_eval']:
for worker in workers:
worker.id = worker_roles[worker.worker_id]
else:
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
conv_idx = mturk_manager.conversation_index
world = WizardEval(
opt=start_opt,
agents=workers,
range_turn=[
int(s) for s in start_opt['range_turn'].split(',')
],
max_turn=start_opt['max_turns'],
max_resp_time=start_opt['max_resp_time'],
model_agent_opt=shared_bot_params,
world_tag='conversation t_{}'.format(conv_idx),
agent_timeout_shutdown=opt['ag_shutdown_time'],
)
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
gc.collect()
mturk_manager.start_task(eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
Wizard of Wikipedia Data Collection Task.
The task involves two people holding a conversation. One dialog partner
chooses a topic to discuss, and then dialog proceeds.
One partner is the Wizard, who has access to retrieved external
information conditioned on the last two utterances, as well as
information regarding the chosen topic.
The other partner is the Apprentice, who assumes the role of someone
eager to learn about the chosen topic.
"""
argparser = ParlaiParser(False, False)
DictionaryAgent.add_cmdline_args(argparser)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument(
'-min_t', '--min_turns', default=3, type=int, help='minimum number of turns'
)
argparser.add_argument(
'-max_t',
'--max_turns',
default=5,
type=int,
help='maximal number of chat turns',
)
argparser.add_argument(
'-mx_rsp_time',
'--max_resp_time',
default=120,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'-mx_onb_time',
'--max_onboard_time',
type=int,
default=300,
help='time limit for turker' 'in onboarding',
)
argparser.add_argument(
'--persona-type',
default='both',
type=str,
choices=['both', 'self', 'other'],
help='Which personas to load from personachat',
)
argparser.add_argument(
'--auto-approve-delay',
type=int,
default=3600 * 24 * 1,
help='how long to wait for \
auto approval',
)
argparser.add_argument(
'--word-overlap-threshold',
type=int,
default=2,
help='How much word overlap we want between message \
and checked sentence',
)
argparser.add_argument(
'--num-good-sentence-threshold',
type=int,
default=2,
help='How many good sentences with sufficient overlap \
are necessary for turker to be considered good.',
)
argparser.add_argument(
'--num-passages-retrieved',
type=int,
default=7,
help='How many passages to retrieve per dialog \
message',
)
opt = argparser.parse_args()
directory_path = os.path.dirname(os.path.abspath(__file__))
opt['task'] = os.path.basename(directory_path)
if 'data_path' not in opt:
opt['data_path'] = os.getcwd() + '/data/' + opt['task']
opt['current_working_dir'] = os.getcwd()
opt.update(task_config)
mturk_agent_ids = [APPRENTICE, WIZARD]
opt['min_messages'] = 2
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
setup_personas_with_wiki_links(opt)
ir_agent, task = setup_retriever(opt)
persona_generator = PersonasGenerator(opt)
wiki_title_to_passage = setup_title_to_passage(opt)
mturk_manager.setup_server(task_directory_path=directory_path)
worker_roles = {}
connect_counter = AttrDict(value=0)
try:
mturk_manager.start_new_run()
if not opt['is_sandbox']:
with open(os.path.join(opt['current_working_dir'], 'mtdont.txt')) as f:
lines = [l.replace('\n', '') for l in f.readlines()]
for w in lines:
mturk_manager.soft_block_worker(w)
def run_onboard(worker):
role = mturk_agent_ids[connect_counter.value % len(mturk_agent_ids)]
connect_counter.value += 1
worker_roles[worker.worker_id] = role
worker.persona_generator = persona_generator
world = RoleOnboardWorld(opt, worker, role)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
mturk_manager.create_hits()
def check_workers_eligibility(workers):
if opt['is_sandbox']:
return workers
valid_workers = {}
for worker in workers:
worker_id = worker.worker_id
if worker_id not in worker_roles:
"""
Something went wrong...
"""
continue
role = worker_roles[worker_id]
if role not in valid_workers:
valid_workers[role] = worker
if len(valid_workers) == 2:
break
return valid_workers.values() if len(valid_workers) == 2 else []
eligibility_function = {'func': check_workers_eligibility, 'multiple': True}
def assign_worker_roles(workers):
if opt['is_sandbox']:
for i, worker in enumerate(workers):
worker.id = mturk_agent_ids[i % len(mturk_agent_ids)]
else:
for worker in workers:
worker.id = worker_roles[worker.worker_id]
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
if not opt['is_sandbox']:
for agent in agents:
worker_roles.pop(agent.worker_id)
conv_idx = mturk_manager.conversation_index
world = MTurkWizardOfWikipediaWorld(
opt,
agents=agents,
world_tag='conversation t_{}'.format(conv_idx),
ir_agent=ir_agent,
wiki_title_to_passage=wiki_title_to_passage,
task=task,
)
world.reset_random()
while not world.episode_done():
world.parley()
world.save_data()
if world.convo_finished and not world.good_wiz and not opt['is_sandbox']:
mturk_manager.soft_block_worker(world.wizard_worker)
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
Human Evaluation of various image captions/comments.
A turker is shown an image and two possible comments/captions, and
optionally the personality used to create these captions. Then, the
turker is asked to choose which caption they think is more engaging.
In this example, we will just be comparing the original comment twice
(this is just to demonstrate the task for future use).
To use your own data, please specify `--eval-data-path` to an
appropriate json file with a list of examples, where each example
has the following structure:
{
'image_hash': <hash of image>,
'personality': <personality, if applicable>,
'<compare_key_1>': <first option to compare>,
'<compare_key_2>': <second option to compare>,
.
.
.
}
Note that compare_key_1 and compare_key_2 can be any field, as long as they
map to a string comment/caption.
Example Scenario:
Suppose you have the original Personality-Captions dataset, and
you would like to compare the outputs of your model called `model`.
Your data may look like the following:
[{
'image_hash': hashforimageofcat,
'personality': 'Sweet',
'comment': 'Look at the cute cat!', # Human Comment
'model_comment': 'That's a weird looking dog' # Model Comment
}, ...]
Thus, you would specify `-ck1 comment -ck2 model_comment` to evaluate
the outputs of the model vs. the human comments from Personality-Captions
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-mx_rsp_time', '--max_resp_time', default=1800,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('-mx_onb_time', '--max_onboard_time', type=int,
default=300, help='time limit for turker'
'in onboarding')
argparser.add_argument('-ni', '--num_images', type=int,
default=10, help='number of images to show \
to turker')
argparser.add_argument('--data-path', type=str,
default='', help='where to save data')
argparser.add_argument('--eval-data-path', type=str, default='',
help='where to load data to rank from. Leave '
'blank to use Personality-Captions data')
argparser.add_argument('-ck1', '--compare-key-1', type=str,
default='comment',
help='key of first option to compare')
argparser.add_argument('-ck2', '--compare-key-2', type=str,
default='comment',
help='key of second option to compare')
argparser.add_argument('--show-personality', default=True, type='bool',
help='whether to show the personality')
PersonalityCaptionsTeacher.add_cmdline_args(argparser)
opt = argparser.parse_args()
directory_path = os.path.dirname(os.path.abspath(__file__))
opt['task'] = os.path.basename(directory_path)
if 'data_path' not in opt or opt['data_path'] == '':
opt['data_path'] = os.getcwd() + '/data/' + opt['task']
if opt.get('eval_data_path') == '':
opt['eval_data_path'] = os.path.join(
opt['datapath'],
'personality_captions/train.json')
opt.update(task_config)
mturk_agent_ids = [CHOOSER]
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
example_generator = ExampleGenerator(opt)
mturk_manager.setup_server(task_directory_path=directory_path)
try:
mturk_manager.start_new_run()
def run_onboard(worker):
worker.example_generator = example_generator
world = RoleOnboardWorld(opt, worker)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
mturk_manager.create_hits()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for w in workers:
w.id = mturk_agent_ids[0]
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
conv_idx = mturk_manager.conversation_index
world = MTurkPersonalityCaptionsStackRankWorld(
opt,
agents=agents,
world_tag='conversation t_{}'.format(conv_idx),
)
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
# The dialog model we want to evaluate
from parlai.agents.ir_baseline.ir_baseline import IrBaselineAgent
IrBaselineAgent.add_cmdline_args(argparser)
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
# The task that we will evaluate the dialog model on
task_opt = {}
task_opt['datatype'] = 'test'
task_opt['datapath'] = opt['datapath']
task_opt['task'] = '#MovieDD-Reddit'
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = ModelEvaluatorOnboardWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_agent = workers[0]
model_agent = IrBaselineAgent(opt=opt)
world = ModelEvaluatorWorld(opt=opt,
model_agent=model_agent,
task_opt=task_opt,
mturk_agent=mturk_agent)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.start_task(eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
Main function for the DMG pilot data collection task
:return: Nothing.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('--two_mturk_agents', dest='two_mturk_agents',
action='store_true', help='data collection mode '
'with converations between two MTurk agents')
opt = argparser.parse_args()
opt['task'] = 'dmg_pilot_dev'
opt['datatype'] = 'dmg_pilot_data_1'
opt.update(task_config)
local_agent_1_id = 'local_1'
mturk_agent_ids = ['mturk_agent_1']
if opt['two_mturk_agents']:
mturk_agent_ids.append('mturk_agent_2')
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
mturk_manager.set_onboard_function(onboard_function=None)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
# Create a local agent
if not opt['two_mturk_agents']:
if 'model' in opt:
local_agent = create_agent(opt)
else:
local_agent = LocalHumanAgent(opt=None)
local_agent.id = local_agent_1_id
agents.append(local_agent)
opt["batchindex"] = mturk_manager.started_conversations
world = MTurkDMGDialogWorld(
opt=opt,
agents=agents
)
log_timestamp = time.time()
# Loop over all five rounds of the game
for r in range(5):
print("--- Starting round {} ---".format(r+1))
while not world.episode_done():
world.parley()
# Write the log data to file
print("Writing log to file")
if not os.path.exists("logs"):
os.makedirs("logs")
with open('logs/dmg_pilot_data_{}_{}.json'.format(world.game_nr, log_timestamp), 'w') as f:
json.dump(world.conversation_log, f)
if not r == 4:
# Reset the world for the next round
world.selections = defaultdict(lambda: dict())
world.round_log = world.reset_round_log()
world.turn_nr = -1
world.round_nr += 1
world.doneCounter = 0
world.episodeDone = False
else:
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
print("Game ended.")
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
This task consists of one local human agent and two MTurk agents,
each MTurk agent will go through the onboarding step to provide
information about themselves, before being put into a conversation.
You can end the conversation by sending a message ending with
`[DONE]` from human_1.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_1_id = 'mturk_agent_1'
mturk_agent_2_id = 'mturk_agent_2'
human_agent_1_id = 'human_1'
mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id]
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = MTurkMultiAgentDialogOnboardWorld(
opt=opt,
mturk_agent=worker
)
while not world.episode_done():
world.parley()
world.shutdown()
# You can set onboard_function to None to skip onboarding
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
eligibility_function = {
'func': check_worker_eligibility,
'multiple': False,
}
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
# Create mturk agents
mturk_agent_1 = workers[0]
mturk_agent_2 = workers[1]
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
world = MTurkMultiAgentDialogWorld(
opt=opt,
agents=[human_agent_1, mturk_agent_1, mturk_agent_2]
)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""This task consists of one agent, model or MTurk worker, talking to an
MTurk worker to negotiate a deal.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('--two_mturk_agents', dest='two_mturk_agents',
action='store_true', help='data collection mode '
'with converations between two MTurk agents')
opt = argparser.parse_args()
opt['task'] = 'dealnodeal'
opt['datatype'] = 'valid'
opt.update(task_config)
local_agent_1_id = 'local_1'
mturk_agent_ids = ['mturk_agent_1']
if opt['two_mturk_agents']:
mturk_agent_ids.append('mturk_agent_2')
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
mturk_manager.set_onboard_function(onboard_function=None)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
# Create a local agent
if not opt['two_mturk_agents']:
if 'model' in opt:
local_agent = create_agent(opt)
else:
local_agent = LocalHumanAgent(opt=None)
local_agent.id = local_agent_1_id
agents.append(local_agent)
opt["batchindex"] = mturk_manager.started_conversations
world = MTurkDealNoDealDialogWorld(
opt=opt,
agents=agents
)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
# The dialog model we want to evaluate
from parlai.agents.ir_baseline.ir_baseline import IrBaselineAgent
IrBaselineAgent.add_cmdline_args(argparser)
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
# The task that we will evaluate the dialog model on
task_opt = {}
task_opt['datatype'] = 'test'
task_opt['datapath'] = opt['datapath']
task_opt['task'] = '#MovieDD-Reddit'
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=[mturk_agent_id]
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = ModelEvaluatorOnboardWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_agent = workers[0]
model_agent = IrBaselineAgent(opt=opt)
world = ModelEvaluatorWorld(
opt=opt,
model_agent=model_agent,
task_opt=task_opt,
mturk_agent=mturk_agent
)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
# Initialize a SQuAD teacher agent, which we will get context from
module_name = 'parlai.tasks.squad.agents'
class_name = 'DefaultTeacher'
my_module = importlib.import_module(module_name)
task_class = getattr(my_module, class_name)
task_opt = {}
task_opt['datatype'] = 'train'
task_opt['datapath'] = opt['datapath']
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=[mturk_agent_id]
)
mturk_manager.setup_server()
def run_onboard(worker):
world = QADataCollectionOnboardWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=None)
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
mturk_manager.ready_to_accept_workers()
def check_workers_eligibility(workers):
return workers
eligibility_function = {
'func': check_workers_eligibility,
'multiple': True,
}
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
task = task_class(task_opt)
mturk_agent = workers[0]
world = QADataCollectionWorld(
opt=opt,
task=task,
mturk_agent=mturk_agent
)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
user_ids = {}
# Initialize a SQuAD teacher agent, which we will get context from
module_name = 'parlai.tasks.squad.agents'
class_name = 'DefaultTeacher'
my_module = importlib.import_module(module_name)
task_class = getattr(my_module, class_name)
task_opt = opt.copy()
task_opt['datatype'] = 'train'
task_opt['datapath'] = opt['datapath']
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
mturk_manager.setup_server()
mturk_manager.set_onboard_function(onboard_function=None)
mturk_manager.start_new_run()
mturk_manager.create_hits()
mturk_manager.ready_to_accept_workers()
def check_workers_eligibility(workers):
return workers
eligibility_function = {
'func': check_workers_eligibility,
'multiple': True,
}
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_manager.left_pane_refresh(task_config['task_description'])
mturk_agent = workers[0]
if (mturk_agent.worker_id in user_ids):
print("USER_ID: ", mturk_agent.worker_id, " DIALS: ",
user_ids[mturk_agent.worker_id])
else:
print("USER_ID: ", mturk_agent.worker_id, " DIALS: 0")
if (mturk_agent.worker_id
in user_ids) and user_ids[mturk_agent.worker_id] >= 15:
ad = {'episode_done': False}
ad['id'] = 'Restaurant bot'
ad['text'] = "We are closing this HIT, since you've already had over 15 dialogues with our restaurant bot in this session. We are very appreciated for your help. Welcome to join the next session."
mturk_agent.observe(validate(ad))
return
else:
world = QADataCollectionWorld(opt=opt, mturk_agent=mturk_agent)
btime = time.time()
world.parley()
etime = time.time()
logger.debug("DialTime: " + str(etime - btime))
if mturk_agent.worker_id not in user_ids:
user_ids[mturk_agent.worker_id] = 1
else:
user_ids[mturk_agent.worker_id] += 1
world.shutdown()
world.review_work()
return
mturk_manager.start_task(eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""Handles setting up and running a ParlAI-MTurk task by instantiating
an MTurk manager and configuring it for the qa_data_collection task
"""
# Get relevant arguments
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
# Set the task name to be the folder name
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt['assignment_duration_in_seconds'] = 10000
# append the contents of task_config.py to the configuration
opt.update(task_config)
# Initialize a teacher agent, which we will get premises from
module_name = 'parlai.tasks.squad2.agents'
class_name = 'DefaultTeacher'
my_module = importlib.import_module(module_name)
task_class = getattr(my_module, class_name)
task_opt = opt.copy()
task_opt['datatype'] = 'train'
task_opt['datapath'] = opt['datapath']
assert (
task_opt['num_workers'] % 2 == 0 and task_opt['num_workers'] >= 4
), "The game only supports even number of workers, with a minimum of 4 people."
# Select an agent_id that worker agents will be assigned in their world
persons = {}
mturk_agent_roles = []
for i in range(1, task_opt['num_workers'] + 1):
persons[i] = 'Person' + str(i)
mturk_agent_roles.append(persons[i])
# Instantiate an MTurkManager with the given options and a maximum number
# of agents per world of 1 (based on the length of mturk_agent_ids)
mturk_manager = MTurkManager(opt=opt,
mturk_agent_ids=mturk_agent_roles,
use_db=True)
mturk_manager.setup_server(
task_directory_path=os.path.dirname(os.path.abspath(__file__)))
role_index = 0
# Create an onboard_function, which will be run for workers who have
# accepted your task and must be completed before they are put in the
# queue for a task world.
def run_onboard(worker):
nonlocal role_index
role = mturk_agent_roles[role_index % len(mturk_agent_roles)]
role_index += 1
worker.update_agent_id('Onboarding {}'.format(role))
worker.demo_role = role
# if role == 'Writer0' or role == 'Writer1':
world = OnboardingWorld(opt=opt, mturk_agent=worker)
# else:
# world = EvaluatorOnboardingWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
return world.prep_save_data([worker])
# If we want to use the above onboard function, we can replace the below
# with set_onboard_function(onboard_function=run_onboard)
mturk_manager.set_onboard_function(onboard_function=run_onboard)
try:
# Initialize run information
mturk_manager.start_new_run()
# Set up the sockets and threads to recieve workers
mturk_manager.ready_to_accept_workers()
# Create the hits as specified by command line arguments
mturk_manager.create_hits()
# Check workers eligiblity acts as a filter, and should return
# the list of all workers currently eligible to work on the task
# Can be used to pair workers that meet certain criterea
def check_workers_eligibility(workers):
filled_roles = []
use_workers = []
for worker in workers:
if worker.demo_role not in filled_roles:
use_workers.append(worker)
filled_roles.append(worker.demo_role)
return use_workers
eligibility_function = {
'func': check_workers_eligibility,
'multiple': True
}
# Assign worker roles is used to determine what the role each worker
# in the given worker list will play. Setting `id` to None will return
# the worker to the pool rather than putting them in a given task,
# which is useful for having tasks with different possible worker
# counts.
def assign_worker_roles(workers):
for worker in workers:
worker.id = worker.demo_role
# Define the task function, which will be run with workers that are
# as the main task.
global run_conversation
def run_conversation(mturk_manager, opt, workers):
# Create a task agent to get prompts from SQuAD 2.0
task = task_class(task_opt)
# Create the task world
world = MultiRoleAgentWorld(opt=opt,
task=task,
mturk_agents=workers)
# run the world to completion
while not world.episode_done():
world.parley()
# shutdown and review the work
world.shutdown()
world.review_work()
# Return the contents for saving
return world.prep_save_data(workers)
# Begin the task, allowing mturk_manager to start running the task
# world on any workers who connect
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
# Any hits that aren't claimed or completed have to be shut down. Must
# keep the world running until that point.
mturk_manager.expire_all_unassigned_hits()
# Shutdown the manager and free all related resources
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_1_id = 'mturk_agent_1'
mturk_agent_2_id = 'mturk_agent_2'
human_agent_1_id = 'human_1'
mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id]
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids = mturk_agent_ids
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = MTurkMultiAgentDialogOnboardWorld(
opt=opt,
mturk_agent=worker
)
while not world.episode_done():
world.parley()
world.shutdown()
# You can set onboard_function to None to skip onboarding
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
# Create mturk agents
mturk_agent_1 = workers[0]
mturk_agent_2 = workers[1]
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
world = MTurkMultiAgentDialogWorld(
opt=opt,
agents=[human_agent_1, mturk_agent_1, mturk_agent_2]
)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
completed_workers = []
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
mturk_manager.setup_server()
qual_name = 'ParlAIExcludeQual{}t{}'.format(random.randint(10000, 99999),
random.randint(10000, 99999))
qual_desc = (
'Qualification for a worker not correctly completing the '
'first iteration of a task. Used to filter to different task pools.')
qualification_id = \
mturk_utils.find_or_create_qualification(qual_name, qual_desc,
opt['is_sandbox'])
print('Created qualification: ', qualification_id)
def run_onboard(worker):
world = QualificationFlowOnboardWorld(opt, worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
try:
mturk_manager.start_new_run()
agent_qualifications = [{
'QualificationTypeId': qualification_id,
'Comparator': 'DoesNotExist',
'RequiredToPreview': True
}]
mturk_manager.create_hits(qualifications=agent_qualifications)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_agent = workers[0]
world = QualificationFlowSoloWorld(
opt=opt,
mturk_agent=mturk_agent,
qualification_id=qualification_id,
firstTime=(mturk_agent.worker_id not in completed_workers),
)
while not world.episode_done():
world.parley()
completed_workers.append(mturk_agent.worker_id)
world.shutdown()
world.review_work()
mturk_manager.start_task(eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except BaseException:
raise
finally:
mturk_utils.delete_qualification(qualification_id, opt['is_sandbox'])
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""This task consists of an MTurk agent evaluating a Controllable Dialog model.
"""
start_time = datetime.datetime.today().strftime('%Y-%m-%d-%H-%M')
argparser = ParlaiParser(False, add_model_args=True)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument(
'--max-resp-time',
default=240,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'--max-choice-time',
type=int,
default=300,
help='time limit for turker' 'choosing the topic',
)
argparser.add_argument(
'--ag-shutdown-time',
default=120,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'--num-turns', default=6, type=int, help='number of turns of dialogue'
)
argparser.add_argument(
'--human-eval',
type='bool',
default=False,
help='human vs human eval, no models involved',
)
argparser.add_argument(
'--auto-approve-delay',
type=int,
default=3600 * 24 * 2,
help='how long to wait for auto approval',
)
argparser.add_argument(
'--only-masters',
type='bool',
default=False,
help='Set to true to use only master turks for ' 'this test eval',
)
argparser.add_argument(
'--create-model-qualif',
type='bool',
default=True,
help='Create model qualif so unique eval between' 'models.',
)
argparser.add_argument(
'--limit-workers',
type=int,
default=len(SETTINGS_TO_RUN),
help='max HITs a worker can complete',
)
argparser.add_argument(
'--mturk-log',
type=str,
default=('data/mturklogs/controllable/{}.log'.format(start_time)),
)
argparser.add_argument(
'--short-eval',
type='bool',
default=True,
help='Only ask engagingness question and persona' 'question.',
)
# persona specific arguments
argparser.add_argument(
'--persona-type', type=str, default='self', choices=['self', 'other', 'none']
)
argparser.add_argument(
'--persona-datatype',
type=str,
default='valid',
choices=['train', 'test', 'valid'],
)
argparser.add_argument(
'--max-persona-time', type=int, default=360, help='max time to view persona'
)
def get_logger(opt):
fmt = '%(asctime)s: [ %(message)s ]'
logfn = None
if 'mturk_log' in opt:
logfn = opt['mturk_log']
if not os.path.isdir(os.path.dirname(logfn)):
os.makedirs(os.path.dirname(logfn), exist_ok=True)
logger = ParlaiLogger(
name="mturk_controllable",
console_level=INFO,
file_level=INFO,
console_format=fmt,
file_format=fmt,
filename=logfn,
)
logger.info('COMMAND: %s' % ' '.join(sys.argv))
logger.info('-' * 100)
logger.info('CONFIG:\n%s' % json.dumps(opt, indent=4, sort_keys=True))
return logger
start_opt = argparser.parse_args()
task_config['task_description'] = task_config['task_description'].format(
start_opt['reward']
)
# set options
start_opt['limit_workers'] = len(SETTINGS_TO_RUN)
start_opt['allowed_conversations'] = 1
start_opt['max_hits_per_worker'] = start_opt['limit_workers']
start_opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
start_opt.update(task_config)
logger = get_logger(start_opt)
model_share_params = {}
worker_models_seen = {}
model_opts = {}
model_counts = {}
lock = Lock()
for setup in SETTINGS_TO_RUN:
assert 'human' not in setup
model_counts[setup] = 0
agent_config = getattr(mcf, setup)
combined_config = copy.deepcopy(start_opt)
for k, v in agent_config.items():
combined_config[k] = v
combined_config['override'][k] = v
folder_name = '{}-{}'.format(setup, start_time)
combined_config['save_data_path'] = os.path.join(
start_opt['datapath'], 'local_controllable_dialogue', folder_name
)
model_opts[setup] = combined_config
bot = create_agent(combined_config, True)
model_share_params[setup] = bot.share()
if not start_opt.get('human_eval'):
mturk_agent_ids = ['PERSON_1']
else:
mturk_agent_ids = ['PERSON_1', 'PERSON_2']
mturk_manager = MTurkManager(opt=start_opt, mturk_agent_ids=mturk_agent_ids)
personas_generator = PersonasGenerator(start_opt)
directory_path = os.path.dirname(os.path.abspath(__file__))
mturk_manager.setup_server(task_directory_path=directory_path)
try:
mturk_manager.start_new_run()
agent_qualifications = []
# assign qualifications
if start_opt['create_model_qualif']:
qual_name = 'ControlEvalRound2'
qual_desc = (
'Qualification to ensure workers complete only a certain'
'number of these HITs'
)
qualification_id = mturk_utils.find_or_create_qualification(
qual_name, qual_desc, False
)
print('Created qualification: ', qualification_id)
start_opt['unique_qualif_id'] = qualification_id
def run_onboard(worker):
worker.personas_generator = personas_generator
world = PersonaAssignWorld(start_opt, worker)
world.parley()
world.shutdown()
def check_worker_eligibility(worker):
worker_id = worker.worker_id
lock.acquire()
retval = len(worker_models_seen.get(worker_id, [])) < len(SETTINGS_TO_RUN)
lock.release()
return retval
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
mturk_manager.create_hits(qualifications=agent_qualifications)
def run_conversation(mturk_manager, opt, workers):
conv_idx = mturk_manager.conversation_index
# gotta find a bot this worker hasn't seen yet
assert len(workers) == 1
worker_id = workers[0].worker_id
lock.acquire()
if worker_id not in worker_models_seen:
worker_models_seen[worker_id] = set()
print("MODELCOUNTS:")
print(pprint.pformat(model_counts))
logger.info("MODELCOUNTS\n" + pprint.pformat(model_counts))
model_options = [
(model_counts[setup_name] + 10 * random.random(), setup_name)
for setup_name in SETTINGS_TO_RUN
if setup_name not in worker_models_seen[worker_id]
]
if not model_options:
lock.release()
logger.error(
"Worker {} already finished all settings! Returning none".format(
worker_id
)
)
return None
_, model_choice = min(model_options)
worker_models_seen[worker_id].add(model_choice)
model_counts[model_choice] += 1
lock.release()
world = ControllableDialogEval(
opt=model_opts[model_choice],
agents=workers,
num_turns=start_opt['num_turns'],
max_resp_time=start_opt['max_resp_time'],
model_agent_opt=model_share_params[model_choice],
world_tag='conversation t_{}'.format(conv_idx),
agent_timeout_shutdown=opt['ag_shutdown_time'],
model_config=model_choice,
)
world.reset_random()
while not world.episode_done():
world.parley()
world.save_data()
lock.acquire()
if not world.convo_finished:
model_counts[model_choice] -= 1
worker_models_seen[worker_id].remove(model_choice)
lock.release()
world.shutdown()
gc.collect()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
Main script for running an eval task against the LIGHT dataset.
special CLI arguments are
--light-eval-task-type [speech, emote, action]
--light-eval-unseen [False, True]
This launches a task that, on a workers first attempt pairs with an entry
from the training set. Then based on if the worker performs above a
specified benchmark, they will either be soft blocked from evaluating or
allowed to try against the test set.
"""
# Get relevant arguments
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.set_defaults(datatype='test:stream')
argparser.add_argument('--light-eval-task-type',
default='speech',
help='Type of task to be evaluating')
argparser.add_argument(
'--light-eval-unseen',
default=False,
type='bool',
help='Evaluate against the unseen test rather than the seen test',
)
opt = argparser.parse_args()
task_opt = opt.copy()
task_opt['task'] = 'light_dialog'
assert opt['light_eval_task_type'] in [
'speech',
'emote',
'action',
], '--light-eval-task-type must be one of speech, emote, or action'
LABEL_TYPE = opt['light_eval_task_type'] # speech, emote, action
TRAIN_TURNS = 7
TRAININGS = 1
MAX_WRONG = 1
if LABEL_TYPE != 'speech':
TRAIN_TURNS = 3
TRAININGS = 2
MAX_WRONG = 3 if LABEL_TYPE == 'emote' else 2
task_opt['light_label_type'] = LABEL_TYPE
task_opt['light_use_action'] = 'all'
task_opt['light_use_cands'] = '20'
task_opt['light_use_emote'] = 'all'
task_opt['light_use_objects'] = True
task_opt['light_use_person_names'] = True
task_opt['light_use_persona'] = 'self'
task_opt['light_use_repeat'] = 'none'
task_opt['light_use_setting'] = True
task_opt['light_use_speech'] = 'all'
task_opt['light_use_current_self_output'] = 'all'
task_opt['light_use_clip_cands'] = 10000
task_opt['light_unseen_test'] = task_opt['light_eval_unseen']
random.seed(10)
agent = RepeatLabelAgent(task_opt)
world = create_task(task_opt, agent)
# Populate dialogues from the LIGHT dataset
samples = []
curr_sample = []
while True:
world.parley()
curr_sample.append(world.acts[0].copy())
if world.acts[0]['episode_done']:
if len(curr_sample) >= TRAIN_TURNS:
samples.append(curr_sample)
curr_sample = []
if world.epoch_done():
break
train_samples = []
task_opt['datatype'] = 'train:stream'
task_opt['light_unseen_test'] = False
agent = RepeatLabelAgent(task_opt)
world = create_task(task_opt, agent)
curr_sample = []
while True:
world.parley()
curr_sample.append(world.acts[0].copy())
if world.acts[0]['episode_done']:
if len(curr_sample) >= TRAIN_TURNS:
train_samples.append(curr_sample)
curr_sample = []
if world.epoch_done() or len(train_samples) > 2000:
break
# Set up temporary pools to pull tasks from
use_train_samples = train_samples.copy()
use_samples = train_samples.copy()
# Set the task name to be the folder name
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
# append the contents of task_config.py to the configuration
opt.update(task_config)
# Select an agent_id that worker agents will be assigned in their world
mturk_agent_roles = [LABEL_TYPE]
opt['assignment_duration_in_seconds'] = 20 * 60
# Instantiate an MTurkManager with the given options and a maximum number
# of agents per world of 1 (based on the length of mturk_agent_ids)
mturk_manager = MTurkManager(opt=opt,
mturk_agent_ids=mturk_agent_roles,
use_db=True)
mturk_manager.setup_server(
task_directory_path=os.path.dirname(os.path.abspath(__file__)))
# Create an onboard_function, which will be run for workers who have
# accepted your task and must be completed before they are put in the
# queue for a task world.
completed_agents = []
completed_train = {}
def run_onboard(worker):
nonlocal completed_agents
if worker.worker_id in completed_agents:
return
else:
world = LightEvalTestWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
if world.did_complete:
completed_agents.append(worker.worker_id)
else:
print(worker.worker_id, 'Failed the onboarding')
world.shutdown()
return world.prep_save_data([worker])
mturk_manager.set_onboard_function(onboard_function=run_onboard)
try:
# Initialize run information
mturk_manager.start_new_run()
# Set up the sockets and threads to recieve workers
mturk_manager.ready_to_accept_workers()
# Create the hits as specified by command line arguments
mturk_manager.create_hits(qualifications=[])
# Check workers eligiblity acts as a filter, and should return
# the list of all workers currently eligible to work on the task
# Can be used to pair workers that meet certain criterea
def check_workers_eligibility(workers):
return workers
eligibility_function = {
'func': check_workers_eligibility,
'multiple': True
}
# Assign worker roles is used to determine what the role each worker
# in the given worker list will play. Setting `id` to None will return
# the worker to the pool rather than putting them in a given task,
# which is useful for having tasks with different possible worker
# counts.
def assign_worker_roles(workers):
workers[0].id = LABEL_TYPE
# Define the task function, which will be run with workers that are
# as the main task.
global run_conversation
def run_conversation(mturk_manager, opt, workers):
nonlocal completed_train
nonlocal use_samples
nonlocal use_train_samples
worker_id = workers[0].worker_id
use_train = True
if worker_id not in completed_train:
completed_train[worker_id] = 0
if completed_train[worker_id] >= TRAININGS:
use_train = False
# Create the real task world
if not use_train:
if len(use_samples) == 0:
# reset the pool if none are left
use_samples = samples.copy()
sample = use_samples.pop()
else:
if len(use_train_samples) == 0:
# reset the pool if none are left
use_train_samples = train_samples.copy()
sample = train_samples.pop()
world = LightEvalTaskWorld(
opt=opt,
mturk_agents=workers,
sample=sample,
use_train=use_train,
max_wrong=MAX_WRONG,
)
# run the world to completion
while not world.episode_done():
world.parley()
# shutdown and review the work
world.shutdown()
world.review_work()
if not world.completed and not use_train:
samples.append(sample)
if use_train and world.completed:
completed_train[worker_id] += 1
print('Worker passed train: ', worker_id)
# Return the contents for saving
return world.prep_save_data(workers)
# Begin the task, allowing mturk_manager to start running the task
# world on any workers who connect
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
print('Accepted agents:', repr(completed_agents))
# Shutdown the manager and free all related resources
mturk_manager.shutdown()
def main():
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
# The dialog model we want to evaluate
from parlai.agents.transformer.transformer import TransformerGeneratorAgent
from parlai.agents.ir_baseline.ir_baseline import IrBaselineAgent
TransformerGeneratorAgent.add_cmdline_args(argparser)
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
# The task that we will evaluate the dialog model on
task_opt = {}
task_opt['datatype'] = 'test'
task_opt['datapath'] = opt['datapath']
task_opt['task'] = 'dailydialog:NoStart'
opt['dict_file'] = '/home/christian/developer/cs767hw4/models/test.dict'
opt['model_file'] = '/home/christian/developer/cs767hw4/models/test.checkpoint'
opt['truncate'] = 64
opt['inference'] = 'mmi'
opt['beam_size'] = 32
opt['no_cuda'] = True
mturk_agent_id = 'Worker'
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
mturk_manager.setup_server() # task_name="dialo-mturk-eval", exists=True
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = ModelEvaluatorOnboardWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(worker):
worker[0].id = mturk_agent_id
global run_conversation
def run_conversation(mturk_manager, opt, workers):
mturk_agent = workers[0]
model_agent = TransformerGeneratorAgent(opt=opt)
world = ModelEvaluatorWorld(
opt=opt,
model_agent=model_agent,
task_opt=task_opt,
mturk_agent=mturk_agent,
)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""This task consists of an MTurk agent evaluating a chit-chat model. They
are asked to chat to the model adopting a specific persona. After their
conversation, they are asked to evaluate their partner on several metrics.
"""
argparser = ParlaiParser(False, add_model_args=True)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument(
'-dp', '--datapath', default='./',
help='path to datasets, defaults to current directory')
opt = argparser.parse_args()
# add additional model args
opt['override'] = {
'no_cuda': True,
'interactive_mode': True,
'tensorboard_log': False
}
# Set the task name to be the folder name
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
# append the contents of task_config.py to the configuration
opt.update(task_config)
mturk_agent_id = 'Tourist'
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=[mturk_agent_id]
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.ready_to_accept_workers()
mturk_manager.create_hits([LOCALE_QUALIF_SDBOX])
mturk_manager.set_onboard_function(onboard_function=None)
# mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
# def assign_worker_roles(workers):
# for index, worker in enumerate(workers):
# worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def assign_worker_roles(workers):
workers[0].id = mturk_agent_id
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
# workers[0].assignment_generator = assignment_generator
world = MultiWozEvalWorld(
opt=opt,
agent=workers[0]
)
while not world.episode_done():
print("parley")
world.parley()
print("save data")
world.save_data()
print("world shutdown")
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
Handles setting up and running a ParlAI-MTurk task by instantiating an MTurk manager
and configuring it for the qa_data_collection task.
"""
# Get relevant arguments
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument(
'--light-unseen-rooms',
default=False,
type='bool',
help='Launch using rooms from the unseen set rather than the seen',
)
opt = argparser.parse_args()
generator = GraphGenerator(opt, opt['light_unseen_rooms'])
# Set the task name to be the folder name
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
# append the contents of task_config.py to the configuration
opt.update(task_config)
# Select an agent_id that worker agents will be assigned in their world
mturk_agent_roles = ['worker_1', 'worker_2']
# Set runtime to be an hour in case workers are slow
opt['assignment_duration_in_seconds'] = 60 * 60
# Instantiate an MTurkManager with the given options and a maximum number
# of agents per world of 1 (based on the length of mturk_agent_ids)
mturk_manager = MTurkManager(
opt=opt, mturk_agent_ids=mturk_agent_roles, use_db=True
)
mturk_manager.setup_server(
task_directory_path=os.path.dirname(os.path.abspath(__file__))
)
# Create an onboard_function, which will be run for workers who have
# accepted your task and must be completed before they are put in the
# queue for a task world.
completed_agents = []
def run_onboard(worker):
nonlocal completed_agents
if worker.worker_id in completed_agents:
return
else:
world = LightChatOnboardingWorld(opt=opt, mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
completed_agents.append(worker.worker_id)
print(worker.worker_id, 'took', world.turns, 'turns for onboarding')
return world.prep_save_data([worker])
# If we want to use the above onboard function, we can replace the below
# with set_onboard_function(onboard_function=run_onboard)
mturk_manager.set_onboard_function(onboard_function=run_onboard)
qualification_id = mturk_utils.find_qualification(
'adventure_chat_reject', opt['is_sandbox'], must_be_owned=False
)
print('Found qualification: ', qualification_id)
try:
# Initialize run information
mturk_manager.start_new_run()
# Set up the sockets and threads to recieve workers
mturk_manager.ready_to_accept_workers()
agent_qualifications = [
{
'QualificationTypeId': qualification_id,
'Comparator': 'DoesNotExist',
'RequiredToPreview': True,
}
]
# Create the hits as specified by command line arguments
mturk_manager.create_hits(qualifications=agent_qualifications)
# Check workers eligiblity acts as a filter, and should return
# the list of all workers currently eligible to work on the task
# Can be used to pair workers that meet certain criterea
def check_workers_eligibility(workers):
return workers
eligibility_function = {'func': check_workers_eligibility, 'multiple': True}
# Assign worker roles is used to determine what the role each worker
# in the given worker list will play. Setting `id` to None will return
# the worker to the pool rather than putting them in a given task,
# which is useful for having tasks with different possible worker
# counts.
def assign_worker_roles(workers):
workers[0].id = mturk_agent_roles[0]
workers[1].id = mturk_agent_roles[1]
# Define the task function, which will be run with workers that are
# as the main task.
global run_conversation
def run_conversation(mturk_manager, opt, workers):
# Create the task world
g = None
while g is None:
try:
g, room, characters = generator.get_room()
except Exception as e:
print('error when creating graph:', repr(e))
world = LightChatTaskWorld(
opt=opt, mturk_agents=workers, graph=g, room=room, characters=characters
)
# run the world to completion
while not world.episode_done():
world.parley()
# shutdown and review the work
world.shutdown()
world.review_work()
# Return the contents for saving
return world.prep_save_data(workers)
# Begin the task, allowing mturk_manager to start running the task
# world on any workers who connect
mturk_manager.start_task(
eligibility_function=eligibility_function,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
# Any hits that aren't claimed or completed have to be shut down. Must
# keep the world running until that point.
mturk_manager.expire_all_unassigned_hits()
# Shutdown the manager and free all related resources
mturk_manager.shutdown()
def main():
"""This task consists of one agent, model or MTurk worker, talking to an
MTurk worker to negotiate a deal.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-min_t', '--min_turns', default=5, type=int,
help='minimum number of turns')
argparser.add_argument('-mt', '--max_turns', default=10, type=int,
help='maximal number of chat turns')
argparser.add_argument('-mx_rsp_time', '--max_resp_time', default=150,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('-mx_psn_time', '--max_persona_time', type=int,
default=300, help='time limit for turker'
'entering the persona')
argparser.add_argument('--ag_shutdown_time', default=120,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('-rp', '--range_persona', default='4,6',
help='sample range of number of persona sentences')
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
if 'data_path' not in opt:
opt['data_path'] = os.getcwd() + '/data/' + opt['task']
opt.update(task_config)
mturk_agent_ids = ['PERSON_1']
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
if not opt['is_sandbox']:
# ADD BLOCKED WORKERS HERE
blocked_worker_list = []
for w in blocked_worker_list:
mturk_manager.block_worker(w, 'We found that you have unexpected behaviors in our previous HITs. For more questions please email us.')
def run_onboard(worker):
pass
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
worker = workers[0]
world = PersonaProfileWorld(opt, worker)
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def setUp(self):
patcher = mock.patch('builtins.input', return_value='y')
self.addCleanup(patcher.stop)
patcher.start()
# Mock functions that hit external APIs and such
self.server_utils = MTurkManagerFile.server_utils
self.mturk_utils = MTurkManagerFile.mturk_utils
self.server_utils.setup_server = mock.MagicMock(
return_value='https://127.0.0.1')
self.server_utils.setup_legacy_server = mock.MagicMock(
return_value='https://127.0.0.1')
self.server_utils.delete_server = mock.MagicMock()
self.mturk_utils.setup_aws_credentials = mock.MagicMock()
self.mturk_utils.calculate_mturk_cost = mock.MagicMock(return_value=1)
self.mturk_utils.check_mturk_balance = mock.MagicMock(
return_value=True)
self.mturk_utils.create_hit_config = mock.MagicMock()
self.mturk_utils.setup_sns_topic = mock.MagicMock(
return_value=TOPIC_ARN)
self.mturk_utils.delete_sns_topic = mock.MagicMock()
self.mturk_utils.delete_qualification = mock.MagicMock()
self.mturk_utils.find_or_create_qualification = mock.MagicMock(
return_value=QUALIFICATION_ID)
self.mturk_utils.find_qualification = mock.MagicMock(
return_value=QUALIFICATION_ID)
self.mturk_utils.give_worker_qualification = mock.MagicMock()
self.mturk_utils.remove_worker_qualification = mock.MagicMock()
self.mturk_utils.create_hit_type = mock.MagicMock(
return_value=HIT_TYPE_ID)
self.mturk_utils.subscribe_to_hits = mock.MagicMock()
self.mturk_utils.create_hit_with_hit_type = mock.MagicMock(
return_value=(MTURK_PAGE_URL, FAKE_HIT_ID, 'MTURK_HIT_DATA'))
self.mturk_utils.get_mturk_client = mock.MagicMock(
return_value=mock.MagicMock())
self.onboarding_agents = {}
self.worlds_agents = {}
# Set up an MTurk Manager and get it ready for accepting workers
self.fake_socket = MockSocket()
time.sleep(0.1)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args()
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 1
self.opt['hit_title'] = 'test_hit_title'
self.opt['hit_description'] = 'test_hit_description'
self.opt['task_description'] = 'test_task_description'
self.opt['hit_keywords'] = 'test_hit_keywords'
self.opt['reward'] = 0.1
self.opt['is_debug'] = True
self.opt['log_level'] = 0
self.opt['num_conversations'] = 1
self.mturk_agent_ids = ['mturk_agent_1', 'mturk_agent_2']
self.mturk_manager = MTurkManager(opt=self.opt,
mturk_agent_ids=self.mturk_agent_ids,
is_test=True)
self.mturk_manager.port = self.fake_socket.port
self.mturk_manager.setup_server()
self.mturk_manager.start_new_run()
self.mturk_manager.ready_to_accept_workers()
self.mturk_manager.set_onboard_function(self.onboard_agent)
self.mturk_manager.create_hits()
def assign_worker_roles(workers):
workers[0].id = 'mturk_agent_1'
workers[1].id = 'mturk_agent_2'
def run_task_wait():
self.mturk_manager.start_task(lambda w: True, assign_worker_roles,
self.run_conversation)
self.task_thread = threading.Thread(target=run_task_wait)
self.task_thread.start()
self.agent_1 = MockAgent(TEST_HIT_ID_1, TEST_ASSIGNMENT_ID_1,
TEST_WORKER_ID_1, TASK_GROUP_ID_1)
self.agent_1_2 = MockAgent(TEST_HIT_ID_1, TEST_ASSIGNMENT_ID_3,
TEST_WORKER_ID_1, TASK_GROUP_ID_1)
self.agent_2 = MockAgent(TEST_HIT_ID_2, TEST_ASSIGNMENT_ID_2,
TEST_WORKER_ID_2, TASK_GROUP_ID_1)
def main():
"""This task consists of one agent, model or MTurk worker, talking to an
MTurk worker to negotiate a deal.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-min_t',
'--min_turns',
default=5,
type=int,
help='minimum number of turns')
argparser.add_argument('-mt',
'--max_turns',
default=10,
type=int,
help='maximal number of chat turns')
argparser.add_argument(
'-mx_rsp_time',
'--max_resp_time',
default=150,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'-mx_psn_time',
'--max_persona_time',
type=int,
default=300,
help='time limit for turker'
'entering the persona',
)
argparser.add_argument(
'--ag_shutdown_time',
default=120,
type=int,
help='time limit for entering a dialog message',
)
argparser.add_argument(
'--persona-type',
default='both',
type=str,
choices=['both', 'self', 'other'],
help='Which personas to load from personachat',
)
argparser.add_argument('--revised',
default=False,
type='bool',
help='Whether to use revised personas')
argparser.add_argument('-rt',
'--range_turn',
default='5,7',
help='sample range of number of turns')
argparser.add_argument('--personas-path',
default=None,
help='specify path for personas data')
opt = argparser.parse_args()
directory_path = os.path.dirname(os.path.abspath(__file__))
opt['task'] = os.path.basename(directory_path)
if not opt.get('personas_path'):
opt['personas_path'] = (argparser.parlai_home +
'/parlai/mturk/personachat_chat/data')
opt.update(task_config)
opt['extract_personas_path'] = os.path.join(opt['datapath'],
'personachat_chat')
mturk_agent_ids = ['PERSON_1', 'PERSON_2']
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
persona_generator = PersonasGenerator(opt)
mturk_manager.setup_server(task_directory_path=directory_path)
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
if not opt['is_sandbox']:
blocked_worker_list = []
for w in blocked_worker_list:
mturk_manager.block_worker(
w,
'We found that you have unexpected behaviors in our previous '
'HITs. For more questions please email us.',
)
def run_onboard(worker):
worker.persona_generator = persona_generator
world = PersonaProfileWorld(opt, worker)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
agents = [workers[0], workers[1]]
conv_idx = mturk_manager.conversation_index
world = PersonaChatWorld(
opt=opt,
agents=agents,
range_turn=[int(s) for s in opt['range_turn'].split(',')],
max_turn=opt['max_turns'],
max_resp_time=opt['max_resp_time'],
world_tag='conversation t_{}'.format(conv_idx),
)
world.reset_random()
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
world.review_work()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation,
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
def main():
"""
This task consists of one local human agent and two MTurk agents,
each MTurk agent will go through the onboarding step to provide
information about themselves, before being put into a conversation.
You can end the conversation by sending a message ending with
`[DONE]` from human_1.
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
opt = argparser.parse_args()
opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
opt.update(task_config)
mturk_agent_1_id = 'mturk_agent_1'
mturk_agent_2_id = 'mturk_agent_2'
human_agent_1_id = 'human_1'
mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id]
mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=mturk_agent_ids)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
mturk_manager.create_hits()
def run_onboard(worker):
world = MTurkMultiAgentDialogOnboardWorld(opt=opt,
mturk_agent=worker)
while not world.episode_done():
world.parley()
world.shutdown()
# You can set onboard_function to None to skip onboarding
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
# Create mturk agents
mturk_agent_1 = workers[0]
mturk_agent_2 = workers[1]
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
world = MTurkMultiAgentDialogWorld(
opt=opt, agents=[human_agent_1, mturk_agent_1, mturk_agent_2])
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.start_task(eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
class TestMTurkAgent(unittest.TestCase):
"""
Various unit tests for the MTurkAgent class.
"""
def setUp(self):
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args(print_args=False)
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(opt=self.opt.copy(),
mturk_agent_ids=mturk_agent_ids)
self.worker_manager = self.mturk_manager.worker_manager
self.turk_agent = MTurkAgent(
self.opt.copy(),
self.mturk_manager,
TEST_HIT_ID_1,
TEST_ASSIGNMENT_ID_1,
TEST_WORKER_ID_1,
)
def tearDown(self):
self.mturk_manager.shutdown()
disconnect_path = os.path.join(parent_dir, 'disconnect-test.pickle')
if os.path.exists(disconnect_path):
os.remove(disconnect_path)
def test_init(self):
"""
Test initialization of an agent.
"""
self.assertIsNotNone(self.turk_agent.creation_time)
self.assertIsNone(self.turk_agent.id)
self.assertIsNone(self.turk_agent.message_request_time)
self.assertIsNone(self.turk_agent.conversation_id)
self.assertFalse(self.turk_agent.some_agent_disconnected)
self.assertFalse(self.turk_agent.hit_is_expired)
self.assertFalse(self.turk_agent.hit_is_abandoned)
self.assertFalse(self.turk_agent.hit_is_returned)
self.assertFalse(self.turk_agent.hit_is_complete)
self.assertFalse(self.turk_agent.disconnected)
self.assertTrue(self.turk_agent.alived)
def test_state_wrappers(self):
"""
Test the mturk agent wrappers around its state.
"""
for status in statuses:
self.turk_agent.set_status(status)
self.assertEqual(self.turk_agent.get_status(), status)
for status in [
AssignState.STATUS_DONE, AssignState.STATUS_PARTNER_DISCONNECT
]:
self.turk_agent.set_status(status)
self.assertTrue(self.turk_agent.submitted_hit())
for status in active_statuses:
self.turk_agent.set_status(status)
self.assertFalse(self.turk_agent.is_final())
for status in complete_statuses:
self.turk_agent.set_status(status)
self.assertTrue(self.turk_agent.is_final())
self.turk_agent.append_message(MESSAGE_1)
self.assertEqual(len(self.turk_agent.get_messages()), 1)
self.turk_agent.append_message(MESSAGE_2)
self.assertEqual(len(self.turk_agent.get_messages()), 2)
self.turk_agent.append_message(MESSAGE_1)
self.assertEqual(len(self.turk_agent.get_messages()), 2)
self.assertIn(MESSAGE_1, self.turk_agent.get_messages())
self.assertIn(MESSAGE_2, self.turk_agent.get_messages())
# Ensure command interactions work as expected
self.turk_agent.set_last_command(COMMAND_1)
self.assertEqual(self.turk_agent.get_last_command(), COMMAND_1)
self.turk_agent.clear_messages()
self.assertEqual(len(self.turk_agent.get_messages()), 0)
# In task checks
self.turk_agent.conversation_id = 't_12345'
self.assertTrue(self.turk_agent.is_in_task())
self.turk_agent.conversation_id = 'b_12345'
self.assertFalse(self.turk_agent.is_in_task())
def test_connection_id(self):
"""
Ensure the connection_id hasn't changed.
"""
connection_id = "{}_{}".format(self.turk_agent.worker_id,
self.turk_agent.assignment_id)
self.assertEqual(self.turk_agent.get_connection_id(), connection_id)
def test_inactive_data(self):
"""
Ensure data packet generated for inactive commands is valid.
"""
for status in complete_statuses:
self.turk_agent.set_status(status)
data = self.turk_agent.get_inactive_command_data()
self.assertIsNotNone(data['text'])
self.assertIsNotNone(data['inactive_text'])
self.assertEqual(data['conversation_id'],
self.turk_agent.conversation_id)
self.assertEqual(data['agent_id'], TEST_WORKER_ID_1)
def test_status_change(self):
has_changed = False
self.turk_agent.set_status(AssignState.STATUS_ONBOARDING)
def wait_for_status_wrap():
nonlocal has_changed # noqa 999 we don't use python2
self.turk_agent.wait_for_status(AssignState.STATUS_WAITING)
has_changed = True
t = threading.Thread(target=wait_for_status_wrap, daemon=True)
t.start()
self.assertFalse(has_changed)
time.sleep(0.07)
self.assertFalse(has_changed)
self.turk_agent.set_status(AssignState.STATUS_WAITING)
time.sleep(0.07)
self.assertTrue(has_changed)
def test_message_queue(self):
"""
Ensure observations and acts work as expected.
"""
self.mturk_manager.send_message = mock.MagicMock()
self.turk_agent.observe(ACT_1)
self.mturk_manager.send_message.assert_called_with(
TEST_WORKER_ID_1, TEST_ASSIGNMENT_ID_1, ACT_1)
# First act comes through the queue and returns properly
self.assertTrue(self.turk_agent.msg_queue.empty())
self.turk_agent.id = AGENT_ID
self.turk_agent.put_data(MESSAGE_ID_1, ACT_1)
self.assertTrue(self.turk_agent.recieved_packets[MESSAGE_ID_1])
self.assertFalse(self.turk_agent.msg_queue.empty())
returned_act = self.turk_agent.get_new_act_message()
self.assertEqual(returned_act, ACT_1)
# Repeat act is ignored
self.turk_agent.put_data(MESSAGE_ID_1, ACT_1)
self.assertTrue(self.turk_agent.msg_queue.empty())
for i in range(100):
self.turk_agent.put_data(str(i), ACT_1)
self.assertEqual(self.turk_agent.msg_queue.qsize(), 100)
self.turk_agent.flush_msg_queue()
self.assertTrue(self.turk_agent.msg_queue.empty())
# Test non-act messages
blank_message = self.turk_agent.get_new_act_message()
self.assertIsNone(blank_message)
self.turk_agent.disconnected = True
disconnect_message = self.turk_agent.get_new_act_message()
self.turk_agent.disconnected = False
self.assertEqual(disconnect_message['text'],
self.turk_agent.MTURK_DISCONNECT_MESSAGE)
self.turk_agent.hit_is_returned = True
return_message = self.turk_agent.get_new_act_message()
self.assertEqual(return_message['text'],
self.turk_agent.RETURN_MESSAGE)
self.turk_agent.hit_is_returned = False
# Reduce state
self.turk_agent.reduce_state()
self.assertIsNone(self.turk_agent.msg_queue)
self.assertIsNone(self.turk_agent.recieved_packets)
def test_message_acts(self):
self.mturk_manager.send_command = mock.MagicMock()
self.mturk_manager.handle_turker_timeout = mock.MagicMock()
# non-Blocking check
self.assertIsNone(self.turk_agent.message_request_time)
returned_act = self.turk_agent.act(blocking=False)
self.assertIsNotNone(self.turk_agent.message_request_time)
self.assertIsNone(returned_act)
self.turk_agent.id = AGENT_ID
self.turk_agent.put_data(MESSAGE_ID_1, ACT_1)
returned_act = self.turk_agent.act(blocking=False)
self.assertIsNone(self.turk_agent.message_request_time)
self.assertEqual(returned_act, ACT_1)
self.mturk_manager.send_command.assert_called_once()
# non-Blocking timeout check
self.mturk_manager.send_command = mock.MagicMock()
returned_act = self.turk_agent.act(timeout=0.07, blocking=False)
self.assertIsNotNone(self.turk_agent.message_request_time)
self.assertIsNone(returned_act)
while returned_act is None:
returned_act = self.turk_agent.act(timeout=0.07, blocking=False)
self.mturk_manager.send_command.assert_called_once()
self.mturk_manager.handle_turker_timeout.assert_called_once()
self.assertEqual(returned_act['text'], self.turk_agent.TIMEOUT_MESSAGE)
# Blocking timeout check
self.mturk_manager.send_command = mock.MagicMock()
self.mturk_manager.handle_turker_timeout = mock.MagicMock()
returned_act = self.turk_agent.act(timeout=0.07)
self.mturk_manager.send_command.assert_called_once()
self.mturk_manager.handle_turker_timeout.assert_called_once()
self.assertEqual(returned_act['text'], self.turk_agent.TIMEOUT_MESSAGE)
def main():
"""This task consists of an MTurk agent evaluating a chit-chat model. They
are asked to chat to the model adopting a specific persona. After their
conversation, they are asked to evaluate their partner on several metrics.
"""
argparser = ParlaiParser(False, add_model_args=True)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
argparser.add_argument('-mt', '--max-turns', default=10, type=int,
help='maximal number of chat turns')
argparser.add_argument('--max-resp-time', default=240,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('--max-persona-time', type=int,
default=300, help='time limit for turker'
'entering the persona')
argparser.add_argument('--ag-shutdown-time', default=120,
type=int,
help='time limit for entering a dialog message')
argparser.add_argument('--persona-type', default='both', type=str,
choices=['both', 'self', 'other'],
help='Which personas to load from personachat')
argparser.add_argument('--revised', default=False, type='bool',
help='Whether to use revised personas')
argparser.add_argument('-rt', '--range-turn', default='5,6',
help='sample range of number of turns')
argparser.add_argument('--auto-approve-delay', type=int,
default=3600 * 24 * 1,
help='how long to wait for auto approval')
argparser.add_argument('--only-masters', type='bool', default=False,
help='Set to True to use only master turks for this' +
' test eval, default is %(default)s')
# ADD MODEL ARGS HERE, UNCOMMENT TO USE KVMEMNN MODEL AS AN EXAMPLE
# argparser.set_defaults(
# model='projects.personachat.kvmemnn.kvmemnn:Kvmemnn',
# model_file='models:convai2/kvmemnn/model',
# )
opt = argparser.parse_args()
# add additional model args
opt['override'] = {
'no_cuda': True,
'interactive_mode': True,
'tensorboard_log': False
}
bot = create_agent(opt)
shared_bot_params = bot.share()
print(
'=== Actual bot opt === :\n {}'.format(
'\n'.join(["[{}] : {}".format(k, v) for k, v in bot.opt.items()])
)
)
folder_name = (
'master_{}_YOURCOMMENT__'.format(opt['only_masters']) +
'__'.join(['{}_{}'.format(k, v) for k, v in opt['override'].items()])
)
# this is mturk task, not convai2 task from ParlAI
opt['task'] = 'convai2:self'
if 'data_path' not in opt:
opt['data_path'] = os.getcwd() + '/data/' + folder_name
opt.update(task_config)
mturk_agent_ids = ['PERSON_1']
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids=mturk_agent_ids
)
persona_generator = PersonasGenerator(opt)
mturk_manager.setup_server()
try:
mturk_manager.start_new_run()
agent_qualifications = []
if opt['only_masters']:
if opt['is_sandbox']:
agent_qualifications.append(MASTER_QUALIF_SDBOX)
else:
agent_qualifications.append(MASTER_QUALIF)
mturk_manager.create_hits(qualifications=agent_qualifications)
if not opt['is_sandbox']:
# ADD SOFT-BLOCKED WORKERS HERE
# NOTE: blocking qual *must be* specified
blocked_worker_list = []
for w in blocked_worker_list:
print('Soft Blocking {}\n'.format(w))
mturk_manager.soft_block_worker(w)
time.sleep(0.1) # do the sleep to prevent amazon query drop
def run_onboard(worker):
worker.persona_generator = persona_generator
world = PersonaProfileWorld(opt, worker)
world.parley()
world.shutdown()
mturk_manager.set_onboard_function(onboard_function=run_onboard)
mturk_manager.ready_to_accept_workers()
def check_worker_eligibility(worker):
return True
def assign_worker_roles(workers):
for index, worker in enumerate(workers):
worker.id = mturk_agent_ids[index % len(mturk_agent_ids)]
def run_conversation(mturk_manager, opt, workers):
agents = workers[0]
conv_idx = mturk_manager.conversation_index
world = Convai2EvalWorld(
opt=opt,
agents=[agents],
range_turn=[int(s) for s in opt['range_turn'].split(',')],
max_turn=opt['max_turns'],
max_resp_time=opt['max_resp_time'],
model_agent_opt=shared_bot_params,
world_tag='conversation t_{}'.format(conv_idx),
agent_timeout_shutdown=opt['ag_shutdown_time'],
)
world.reset_random()
while not world.episode_done():
world.parley()
world.save_data()
world.shutdown()
mturk_manager.start_task(
eligibility_function=check_worker_eligibility,
assign_role_function=assign_worker_roles,
task_function=run_conversation
)
except BaseException:
raise
finally:
mturk_manager.expire_all_unassigned_hits()
mturk_manager.shutdown()
class TestAssignState(unittest.TestCase):
"""
Various unit tests for the AssignState class.
"""
def setUp(self):
self.agent_state1 = AssignState()
self.agent_state2 = AssignState(status=AssignState.STATUS_IN_TASK)
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()
self.opt = argparser.parse_args(print_args=False)
self.opt['task'] = 'unittest'
self.opt['assignment_duration_in_seconds'] = 6
mturk_agent_ids = ['mturk_agent_1']
self.mturk_manager = MTurkManager(opt=self.opt,
mturk_agent_ids=mturk_agent_ids)
self.worker_manager = self.mturk_manager.worker_manager
def tearDown(self):
self.mturk_manager.shutdown()
def test_assign_state_init(self):
"""
Test proper initialization of assignment states.
"""
self.assertEqual(self.agent_state1.status, AssignState.STATUS_NONE)
self.assertEqual(len(self.agent_state1.messages), 0)
self.assertEqual(len(self.agent_state1.message_ids), 0)
self.assertIsNone(self.agent_state1.last_command)
self.assertEqual(self.agent_state2.status, AssignState.STATUS_IN_TASK)
self.assertEqual(len(self.agent_state1.messages), 0)
self.assertEqual(len(self.agent_state1.message_ids), 0)
self.assertIsNone(self.agent_state1.last_command)
def test_message_management(self):
"""
Test message management in an AssignState.
"""
# Ensure message appends succeed and are idempotent
self.agent_state1.append_message(MESSAGE_1)
self.assertEqual(len(self.agent_state1.get_messages()), 1)
self.agent_state1.append_message(MESSAGE_2)
self.assertEqual(len(self.agent_state1.get_messages()), 2)
self.agent_state1.append_message(MESSAGE_1)
self.assertEqual(len(self.agent_state1.get_messages()), 2)
self.assertEqual(len(self.agent_state2.get_messages()), 0)
self.assertIn(MESSAGE_1, self.agent_state1.get_messages())
self.assertIn(MESSAGE_2, self.agent_state1.get_messages())
self.assertEqual(len(self.agent_state1.message_ids), 2)
self.agent_state2.append_message(MESSAGE_1)
self.assertEqual(len(self.agent_state2.message_ids), 1)
# Ensure command interactions work as expected
self.agent_state1.set_last_command(COMMAND_1)
self.assertEqual(self.agent_state1.get_last_command(), COMMAND_1)
# Ensure clearing messages acts as intended and doesn't clear agent2
self.agent_state1.clear_messages()
self.assertEqual(len(self.agent_state1.messages), 0)
self.assertEqual(len(self.agent_state1.message_ids), 0)
self.assertIsNone(self.agent_state1.last_command)
self.assertEqual(len(self.agent_state2.message_ids), 1)
def test_state_handles_status(self):
"""
Ensures status updates and is_final are valid.
"""
for status in statuses:
self.agent_state1.set_status(status)
self.assertEqual(self.agent_state1.get_status(), status)
for status in active_statuses:
self.agent_state1.set_status(status)
self.assertFalse(self.agent_state1.is_final())
for status in complete_statuses:
self.agent_state1.set_status(status)
self.assertTrue(self.agent_state1.is_final())
# TODO update the below once bonus is default
for status in complete_statuses:
self.agent_state1.set_status(status)
text, command = self.agent_state1.get_inactive_command_text()
self.assertIsNotNone(text)
self.assertIsNotNone(command)
