def get_problem_list(request):
"""
Get the list of problems that need grading in course request.GET['course_id'].
Returns:
list of dicts with keys
'location'
'problem_name'
'num_graded' -- number graded
'num_pending' -- number pending in the queue
'min_for_ml' -- minimum needed to make ML model
"""
if request.method!="GET":
error_message="Request needs to be GET."
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
course_id=request.GET.get("course_id")
if not course_id:
error_message="Missing needed tag course_id"
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
locations_for_course = [x['location'] for x in
list(Submission.objects.filter(course_id=course_id).values('location').distinct())]
if len(locations_for_course)==0:
error_message="No problems associated with course."
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
location_info=[]
for location in locations_for_course:
problem_name = Submission.objects.filter(location=location)[0].problem_id
submissions_pending = staff_grading_util.submissions_pending_instructor(location, state_in=[SubmissionState.waiting_to_be_graded]).count()
finished_instructor_graded = staff_grading_util.finished_submissions_graded_by_instructor(location).count()
min_scored_for_location=settings.MIN_TO_USE_PEER
location_ml_count = Submission.objects.filter(location=location, preferred_grader_type="ML").count()
if location_ml_count>0:
min_scored_for_location=settings.MIN_TO_USE_ML
submissions_required = max([0,min_scored_for_location-finished_instructor_graded])
problem_name_from_location=location.split("://")[1]
location_dict={
'location' : location,
'problem_name' : problem_name,
'problem_name_from_location' : problem_name_from_location,
'num_graded' : finished_instructor_graded,
'num_pending' : submissions_pending,
'num_required' : submissions_required,
'min_for_ml' : settings.MIN_TO_USE_ML,
}
location_info.append(location_dict)
util.log_connection_data()
return util._success_response({'problem_list' : location_info},
_INTERFACE_VERSION)
def get_next_submission(request):
"""
Supports GET request with the following arguments:
course_id -- the course for which to return a submission.
grader_id -- LMS user_id of the requesting user
Returns json dict with the following keys:
version: '1' (number)
success: bool
if success:
'submission_id': a unique identifier for the submission, to be passed
back with the grade.
'submission': the submission, rendered as read-only html for grading
'rubric': the rubric, also rendered as html.
'prompt': the question prompt, also rendered as html.
'message': if there was no submission available, but nothing went wrong,
there will be a message field.
else:
'error': if success is False, will have an error message with more info.
}
"""
if request.method != "GET":
raise Http404
course_id = request.GET.get('course_id')
grader_id = request.GET.get('grader_id')
location = request.GET.get('location')
log.debug(
"Getting next submission for instructor grading for course: {0}.".
format(course_id))
if not (course_id or location) or not grader_id:
return util._error_response("required_parameter_missing",
_INTERFACE_VERSION)
if location:
(
found, id
) = staff_grading_util.get_single_instructor_grading_item_for_location(
location)
# TODO: save the grader id and match it in save_grade to make sure things
# are consistent.
if not location:
(found,
id) = staff_grading_util.get_single_instructor_grading_item(course_id)
if not found:
return util._success_response(
{'message': 'No more submissions to grade.'}, _INTERFACE_VERSION)
try:
submission = Submission.objects.get(id=int(id))
except Submission.DoesNotExist:
log.error("Couldn't find submission %s for instructor grading", id)
return util._error_response('failed_to_load_submission',
_INTERFACE_VERSION,
data={'submission_id': id})
#Get error metrics from ml grading, and get into dictionary form to pass down to staff grading view
success, ml_error_info = ml_grading_util.get_ml_errors(submission.location)
if success:
ml_error_message = staff_grading_util.generate_ml_error_message(
ml_error_info)
else:
ml_error_message = ml_error_info
ml_error_message = "Machine learning error information: " + ml_error_message
if submission.state != 'C':
log.error(
"Instructor grading got a submission (%s) in an invalid state: ",
id, submission.state)
return util._error_response('wrong_internal_state',
_INTERFACE_VERSION,
data={
'submission_id': id,
'submission_state': submission.state
})
num_graded, num_pending = staff_grading_util.count_submissions_graded_and_pending_instructor(
submission.location)
response = {
'submission_id':
id,
'submission':
submission.student_response,
'rubric':
submission.rubric,
'prompt':
submission.prompt,
'max_score':
submission.max_score,
'ml_error_info':
ml_error_message,
'problem_name':
submission.problem_id,
'num_graded':
staff_grading_util.finished_submissions_graded_by_instructor(
submission.location).count(),
'num_pending':
staff_grading_util.submissions_pending_instructor(
submission.location,
state_in=[SubmissionState.waiting_to_be_graded]).count(),
'min_for_ml':
settings.MIN_TO_USE_ML,
}
util.log_connection_data()
log.debug("Sending success response back to instructor grading!")
log.debug("Sub id from get next: {0}".format(submission.id))
return util._success_response(response, _INTERFACE_VERSION)
def get_problem_list(request):
"""
Get the list of problems that need grading in course request.GET['course_id'].
Returns:
list of dicts with keys
'location'
'problem_name'
'num_graded' -- number graded
'num_pending' -- number pending in the queue
'min_for_ml' -- minimum needed to make ML model
"""
if request.method != "GET":
error_message = "Request needs to be GET."
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
course_id = request.GET.get("course_id")
if not course_id:
error_message = "Missing needed tag course_id"
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
locations_for_course = [
x['location'] for x in list(
Submission.objects.filter(
course_id=course_id).values('location').distinct())
]
if len(locations_for_course) == 0:
error_message = "No problems associated with course."
log.error(error_message)
return util._error_response(error_message, _INTERFACE_VERSION)
location_info = []
for location in locations_for_course:
problem_name = Submission.objects.filter(
location=location)[0].problem_id
submissions_pending = staff_grading_util.submissions_pending_instructor(
location, state_in=[SubmissionState.waiting_to_be_graded]).count()
finished_instructor_graded = staff_grading_util.finished_submissions_graded_by_instructor(
location).count()
min_scored_for_location = settings.MIN_TO_USE_PEER
location_ml_count = Submission.objects.filter(
location=location, preferred_grader_type="ML").count()
if location_ml_count > 0:
min_scored_for_location = settings.MIN_TO_USE_ML
submissions_required = max(
[0, min_scored_for_location - finished_instructor_graded])
problem_name_from_location = location.split("://")[1]
location_dict = {
'location': location,
'problem_name': problem_name,
'problem_name_from_location': problem_name_from_location,
'num_graded': finished_instructor_graded,
'num_pending': submissions_pending,
'num_required': submissions_required,
'min_for_ml': settings.MIN_TO_USE_ML,
}
location_info.append(location_dict)
util.log_connection_data()
log.debug(location_info)
return util._success_response({'problem_list': location_info},
_INTERFACE_VERSION)
def get_next_submission(request):
"""
Supports GET request with the following arguments:
course_id -- the course for which to return a submission.
grader_id -- LMS user_id of the requesting user
Returns json dict with the following keys:
version: '1' (number)
success: bool
if success:
'submission_id': a unique identifier for the submission, to be passed
back with the grade.
'submission': the submission, rendered as read-only html for grading
'rubric': the rubric, also rendered as html.
'prompt': the question prompt, also rendered as html.
'message': if there was no submission available, but nothing went wrong,
there will be a message field.
else:
'error': if success is False, will have an error message with more info.
}
"""
if request.method != "GET":
raise Http404
course_id = request.GET.get('course_id')
grader_id = request.GET.get('grader_id')
location = request.GET.get('location')
if not (course_id or location) or not grader_id:
return util._error_response("required_parameter_missing", _INTERFACE_VERSION)
if location:
(found, id) = staff_grading_util.get_single_instructor_grading_item_for_location(location)
# TODO: save the grader id and match it in save_grade to make sure things
# are consistent.
if not location:
(found, id) = staff_grading_util.get_single_instructor_grading_item(course_id)
if not found:
return util._success_response({'message': 'No more submissions to grade.'},
_INTERFACE_VERSION)
try:
submission = Submission.objects.get(id=int(id))
except Submission.DoesNotExist:
log.error("Couldn't find submission %s for instructor grading", id)
return util._error_response('failed_to_load_submission',
_INTERFACE_VERSION,
data={'submission_id': id})
#Get error metrics from ml grading, and get into dictionary form to pass down to staff grading view
success, ml_error_info=ml_grading_util.get_ml_errors(submission.location)
if success:
ml_error_message=staff_grading_util.generate_ml_error_message(ml_error_info)
else:
ml_error_message=ml_error_info
ml_error_message="Machine learning error information: " + ml_error_message
if submission.state != 'C':
log.error("Instructor grading got a submission (%s) in an invalid state: ",
id, submission.state)
return util._error_response('wrong_internal_state',
_INTERFACE_VERSION,
data={'submission_id': id,
'submission_state': submission.state})
num_graded, num_pending = staff_grading_util.count_submissions_graded_and_pending_instructor(submission.location)
response = {'submission_id': id,
'submission': submission.student_response,
'rubric': submission.rubric,
'prompt': submission.prompt,
'max_score': submission.max_score,
'ml_error_info' : ml_error_message,
'problem_name' : submission.problem_id,
'num_graded' : staff_grading_util.finished_submissions_graded_by_instructor(submission.location).count(),
'num_pending' : staff_grading_util.submissions_pending_instructor(submission.location,
state_in=[SubmissionState.waiting_to_be_graded]).count(),
'min_for_ml' : settings.MIN_TO_USE_ML,
}
util.log_connection_data()
return util._success_response(response, _INTERFACE_VERSION)
