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'
human_agent_2_id = 'human_2'
mturk_manager = MTurkManager(
opt=opt, mturk_agent_ids=[mturk_agent_1_id, mturk_agent_2_id])
mturk_manager.init_aws(opt=opt)
mturk_manager.start_new_run(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, opt, mturk_manager):
# Create mturk agents
mturk_agent_1 = MTurkAgent(id=mturk_agent_1_id,
manager=mturk_manager,
hit_index=hit_index,
assignment_index=assignment_index,
opt=opt)
mturk_agent_2 = MTurkAgent(id=mturk_agent_2_id,
manager=mturk_manager,
hit_index=hit_index,
assignment_index=assignment_index,
opt=opt)
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
human_agent_2 = LocalHumanAgent(opt=None)
human_agent_2.id = human_agent_2_id
world = MTurkMultiAgentDialogWorld(opt=opt,
agents=[
human_agent_1, human_agent_2,
mturk_agent_1, mturk_agent_2
])
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
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(
mturk_agent_ids=[mturk_agent_id],
all_agent_ids=[ModelEvaluatorWorld.evaluator_agent_id,
mturk_agent_id] # In speaking order
)
mturk_manager.init_aws(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, opt, task_opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
model_agent = IrBaselineAgent(opt=opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id=mturk_agent_id,
manager=mturk_manager,
conversation_id=conversation_id,
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()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, opt, task_opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
mturk_manager.review_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.getcwd())
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']
global run_hit
def run_hit(i, task_class, task_opt, opt, mturk_manager):
task = task_class(task_opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id='Worker', manager=mturk_manager, conversation_id=i, opt=opt)
world = QADataCollectionWorld(opt=opt, task=task, mturk_agent=mturk_agent)
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager = MTurkManager()
mturk_manager.init_aws(opt=opt)
results = Parallel(n_jobs=opt['num_hits'], backend='threading')(delayed(run_hit)(i, task_class, task_opt, opt, mturk_manager) for i in range(1, opt['num_hits']+1))
mturk_manager.review_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.init_aws(opt=opt)
mturk_manager.start_new_run(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, task_class, task_opt, opt,
mturk_manager):
task = task_class(task_opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id=mturk_agent_id,
manager=mturk_manager,
hit_index=hit_index,
assignment_index=assignment_index,
opt=opt)
world = QADataCollectionWorld(opt=opt,
task=task,
mturk_agent=mturk_agent)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, task_class, task_opt, opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
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.getcwd())
opt.update(task_config)
global run_hit
def run_hit(i, opt, mturk_manager):
# Create mturk agents
mturk_agent_1 = MTurkAgent(id='mturk_agent_1',
manager=mturk_manager,
conversation_id=i,
opt=opt)
mturk_agent_2 = MTurkAgent(id='mturk_agent_2',
manager=mturk_manager,
conversation_id=i,
opt=opt)
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = 'human_1'
human_agent_2 = LocalHumanAgent(opt=None)
human_agent_2.id = 'human_2'
world = MultiAgentDialogWorld(opt=opt,
agents=[
human_agent_1, human_agent_2,
mturk_agent_1, mturk_agent_2
])
# Since we are using the regular MultiAgentDialogWorld, we do the following outside of the world instead.
mturk_agent_ids = [mturk_agent_1.id, mturk_agent_2.id]
all_agent_ids = [human_agent_1.id, human_agent_2.id] + mturk_agent_ids
mturk_agent_1.mturk_agent_ids = mturk_agent_ids
mturk_agent_1.all_agent_ids = all_agent_ids
mturk_agent_2.mturk_agent_ids = mturk_agent_ids
mturk_agent_2.all_agent_ids = all_agent_ids
mturk_agent_1.create_hit()
mturk_agent_2.create_hit()
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager = MTurkManager()
mturk_manager.init_aws(opt=opt)
results = Parallel(n_jobs=opt['num_hits'], backend='threading')(
delayed(run_hit)(i, opt, mturk_manager)
for i in range(1, opt['num_hits'] + 1))
mturk_manager.review_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.getcwd())
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'
global run_hit
def run_hit(i, opt, task_opt, mturk_manager):
model_agent = IrBaselineAgent(opt=opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id='Worker',
manager=mturk_manager,
conversation_id=i,
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()
mturk_manager = MTurkManager()
mturk_manager.init_aws(opt=opt)
results = Parallel(n_jobs=opt['num_hits'], backend='threading')(
delayed(run_hit)(i, opt, task_opt, mturk_manager)
for i in range(1, opt['num_hits'] + 1))
mturk_manager.review_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],
all_agent_ids = [ModelEvaluatorWorld.evaluator_agent_id, mturk_agent_id] # In speaking order
)
mturk_manager.init_aws(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, opt, task_opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
model_agent = IrBaselineAgent(opt=opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id=mturk_agent_id, manager=mturk_manager, conversation_id=conversation_id, 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.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, opt, task_opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
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'
human_agent_2_id = 'human_2'
mturk_manager = MTurkManager(
opt=opt,
mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id],
all_agent_ids = [human_agent_1_id, human_agent_2_id, mturk_agent_1_id, mturk_agent_2_id] # In speaking order
)
mturk_manager.init_aws(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
# Create mturk agents
mturk_agent_1 = MTurkAgent(id=mturk_agent_1_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
mturk_agent_2 = MTurkAgent(id=mturk_agent_2_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
human_agent_2 = LocalHumanAgent(opt=None)
human_agent_2.id = human_agent_2_id
world = MultiAgentDialogWorld(opt=opt, agents=[human_agent_1, human_agent_2, mturk_agent_1, mturk_agent_2])
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
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],
all_agent_ids = [QADataCollectionWorld.collector_agent_id, mturk_agent_id] # In speaking order
)
mturk_manager.init_aws(opt=opt)
global run_hit
def run_hit(hit_index, assignment_index, task_class, task_opt, opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
task = task_class(task_opt)
# Create the MTurk agent which provides a chat interface to the Turker
mturk_agent = MTurkAgent(id=mturk_agent_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
world = QADataCollectionWorld(opt=opt, task=task, mturk_agent=mturk_agent)
while not world.episode_done():
world.parley()
world.shutdown()
world.review_work()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, task_class, task_opt, opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
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.getcwd())
opt.update(task_config)
mturk_manager = MTurkManager()
mturk_manager.init_aws(opt=opt)
mturk_agent_1_id = 'mturk_agent_1'
mturk_agent_2_id = 'mturk_agent_2'
human_agent_1_id = 'human_1'
human_agent_2_id = 'human_2'
mturk_manager.mturk_agent_ids = [mturk_agent_1_id, mturk_agent_2_id]
mturk_manager.all_agent_ids = [human_agent_1_id, human_agent_2_id] + mturk_manager.mturk_agent_ids # In speaking order
global run_hit
def run_hit(hit_index, assignment_index, opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
# Create mturk agents
mturk_agent_1 = MTurkAgent(id=mturk_agent_1_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
mturk_agent_2 = MTurkAgent(id=mturk_agent_2_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
human_agent_2 = LocalHumanAgent(opt=None)
human_agent_2.id = human_agent_2_id
world = MultiAgentDialogWorld(opt=opt, agents=[human_agent_1, human_agent_2, mturk_agent_1, mturk_agent_2])
while not world.episode_done():
world.parley()
world.shutdown()
mturk_manager.create_hits(opt=opt)
results = Parallel(n_jobs=opt['num_hits'] * opt['num_assignments'], backend='threading') \
(delayed(run_hit)(hit_index, assignment_index, opt, mturk_manager) \
for hit_index, assignment_index in product(range(1, opt['num_hits']+1), range(1, opt['num_assignments']+1)))
mturk_manager.review_hits()
mturk_manager.shutdown()