def create_qualification_typeID_boto2(client):
logfile = open(
get_log_directory('Qualification_boto2') + get_timestamp() + '.txt',
'w')
response = client.create_qualification_type(
name=get_timestamp(),
description=
"Only workers affected by our system could work on this task.",
status="Active",
auto_granted=True,
auto_granted_value=1)
logfile.write(
"Your Qualification is created. Your Qualification Type ID is:\n")
logfile.write(response[0].QualificationTypeId)
# client.assign_qualification(response[0].QualificationTypeId, 'A2MGXHBK15GC8Y', value=1, send_notification=True)
# client.assign_qualification(response[0].QualificationTypeId, 'A2BA16GUOB0DT5', value=1, send_notification=True)
# client.assign_qualification(response[0].QualificationTypeId, 'A3D1A336ZEGP79', value=1, send_notification=True)
return response[0].QualificationTypeId
# if __name__ == '__main__':
# client = get_client('sandbox')
# create_qualification_typeID(client)
def store_assignement_info_on_submission(client):
mongo_client = MongoClient('localhost', 8081)
db = mongo_client.meteor
collection = db[FEEDBACK_COLLECTION]
while (True):
time.sleep(10)
with open(
get_log_directory('CompHIT_submission') + '/records.txt',
'a') as logfile:
assignment_info = get_workerid_assignmentid(client)
for worker_id in assignment_info:
result = is_worker_info_available(worker_id, collection)
if not result:
assignment_id = assignment_info[worker_id]['assignmentID']
feedback = assignment_info[worker_id]['feedback']
document = {
'workerID': worker_id,
'assignment_id': assignment_id,
'feedback': feedback
}
collection.insert_one(document)
logfile.write("Worker %s has been checked at %s.\n" %
(worker_id, get_timestamp()))
def main(environment):
client = get_client(environment)
qualification_type_id = create_qualification_typeID(client)
# worker_id_list = get_worker_id()
# worker_id_list = ['A2MGXHBK15GC8Y']
# worker_id_list = ['A2MGXHBK15GC8Y', 'A3VOSKJ5LS9WB', 'A389861VXHBHWU']
# create a HIT for A3O4TI9D8EPCL7, A1TGV7LT6LTIQU who made mistakes in compensation HIT
worker_id_list = ['A3O4TI9D8EPCL7', 'A1TGV7LT6LTIQU']
print(len(worker_id_list))
logfile = open(
get_log_directory('CompensationHIT') + get_timestamp() + '.txt', 'w')
CompHITlog = open(
get_log_directory('CompensationHIT') + '/records2.txt', 'w')
response = create_hit(qualification_type_id, environment,
len(worker_id_list))
HIT_URL = get_URL_parameters(environment) + response['HIT']['HITGroupId']
HIT_ID = response['HIT']['HITId']
print(HIT_URL + "\n")
print("HITID = " + HIT_ID)
logfile.write(HIT_URL + "\n")
logfile.write("HITID = " + HIT_ID)
CompHITlog.write(HIT_ID)
for worker_id in worker_id_list:
assign(client, worker_id, qualification_type_id)
send_worker_message(client, worker_id, HIT_URL)
def pay_worker_bonus(client, worker_id, total_money, logfile):
workerid_assignmentid_dict = get_workerid_assignmentid(client)
assignment_id = workerid_assignmentid_dict[worker_id]['assignmentID']
feedback = workerid_assignmentid_dict[worker_id]['feedback']
# https://boto3.readthedocs.io/en/latest/reference/services/mturk.html#MTurk.Client.send_bonus
client.send_bonus(WorkerId=worker_id, AssignmentId=assignment_id, BonusAmount=total_money, Reason=MESSAGE)
store_feedback_in_db(worker_id, assignment_id, feedback, total_money, get_timestamp())
logfile.write('Paid worker %s for AssignmentId %s: $%s at %s\n' % (worker_id, assignment_id, total_money, get_timestamp()))
def delete_all_hits(client):
logfile = open(
get_log_directory('DeletionLog') + get_timestamp() + '.txt', 'w')
logfile.write("HITs deleted in this batch:\n")
response = client.get_all_hits()
hit_id_list = list()
for hit in response:
hit_id_list.append(hit.HITId)
for hit_id in tqdm(hit_id_list):
delete_hit(client, hit_id)
logfile.write(hit_id + "\n")
def save_model(self, model):
#Create directory to store results
time = get_timestamp()
self._result_dir = f"results/training_run_{time}"
[os.mkdir(x) for x in [f"{self._result_dir}/{subdir}" for subdir in ["", "model", "plots"]]]
#Save in TF savedModel format
model.save(f'{self._result_dir}/model')
#Freeze model as protobuf (.pb), launches another clean process to take care of it. See model_freeze.py for
#more explanation. If sys.prefix points to wrong python executable, this will probably fail in an odd manner.
python_exec = f'{sys.prefix}/bin/python'
os.system(f'{python_exec} model_freeze.py {self._result_dir}')
def create_qualification_typeID(client):
logfile = open(
get_log_directory('Qualification') + get_timestamp() + '.txt', 'w')
response = client.create_qualification_type(
Name=get_timestamp(),
Description=
"Only workers affected by our system could work on this task.",
QualificationTypeStatus="Active",
AutoGranted=True,
AutoGrantedValue=1)
logfile.write(
"Your Qualification is created. Your Qualification Type ID is:\n")
logfile.write(response['QualificationType']['QualificationTypeId'])
# assign(client, 'A2MGXHBK15GC8Y', response['QualificationType']['QualificationTypeId'])
# assign(client, 'A3VOSKJ5LS9WB', response['QualificationType']['QualificationTypeId'])
# assign(client, 'A2BA16GUOB0DT5', response['QualificationType']['QualificationTypeId'])
# assign(client, 'A3D1A336ZEGP79', response['QualificationType']['QualificationTypeId'])
return response['QualificationType']['QualificationTypeId']
def process_inspire():
"""Returns a quote of correct sentiment and store analyses info in db."""
#getting twitter_handle & user_id from session
twitter_handle = session["twitter_handle"]
user_id = session["user_id"]
# get timestamp for storing to db when user clicks button
timestamp = get_timestamp()
quote_to_send = {}
# calling get_quote function from twitter_analysis, passing in the twitter_handle
# saving it to the quote_to_send dictionary as a value with the key "quote"
quote_info = get_quote(twitter_handle, user_id)
if len(quote_info) > 1:
#adding the actual quote itself to a dictionary to send via JSON
quote_to_send["quote"] = quote_info[0]
# getting quote_id, sentiment to add to db
quote_id = quote_info[1]
sentiment = quote_info[2]
#Part 2: send data to store in analyses in db
# Store: user_id(y), timestamp(y), tweet_sent_id(y), quote_id(y)
# adding analysis instance to the database
analysis = Analyses(user_id=user_id,
timestamp=timestamp,
tweet_sent_id=sentiment,
quote_id=quote_id)
db.session.add(analysis)
db.session.commit()
else:
quote_to_send["quote"] = quote_info[0]
return jsonify(quote_to_send)
number_of_tweets = 0
if data_type is None:
create_document(hit_id)
if data_type == "crowdflower":
number_of_tweets = create_crowdflower_document(hit_id, start_position,
tweet_count)
logfile.write("Your HIT ID is: {}\n\n".format(hit_id))
return hit_type_id, number_of_tweets
if __name__ == '__main__':
argument_length = len(sys.argv)
logfile = open(get_log_directory('HITs') + get_timestamp() + '.txt', 'w')
hit_type_id = ""
if argument_length < 4:
print("4 arguments required ....\n"
"example: python script.py sandbox crowdflower 5 10")
sys.exit(0)
environment = sys.argv[1]
client = get_client(environment)
if sys.argv[2] == 'unlabeled':
hit_type_id = create_hit(client, logfile, sys.argv[2])
if sys.argv[2] == 'crowdflower':
# qualification_type_id = create_qualification_typeID_boto2(client)