def map(cls, event):
"""Extract question responses from all event types providing them."""
if event.source not in (
'submit-assessment',
'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
valid_question_ids = params['valid_question_ids']
group_to_questions = params['group_to_questions']
assessment_weights = params['assessment_weights']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
yield (RawAnswersGenerator.TOTAL_STUDENTS, event.user_id)
# Each answer is a namedtuple; convert to a list for pack/unpack
# journey through the map/reduce shuffle stage.
result = [list(answer) for answer in answers]
for key in cls._generate_keys(event, event.user_id):
yield (key, result)
def map(cls, event):
"""Extract question responses from all event types providing them."""
if event.source not in ('submit-assessment', 'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
valid_question_ids = params['valid_question_ids']
group_to_questions = params['group_to_questions']
assessment_weights = params['assessment_weights']
timestamp = int((event.recorded_on -
datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids,
assessment_weights, group_to_questions, timestamp)
yield (RawAnswersGenerator.TOTAL_STUDENTS, event.user_id)
# Each answer is a namedtuple; convert to a list for pack/unpack
# journey through the map/reduce shuffle stage.
result = [list(answer) for answer in answers]
for key in cls._generate_keys(event, event.user_id):
yield (key, result)
def map(event):
"""Extract question responses from all event types providing them."""
if event.source not in (
'submit-assessment',
'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
group_to_questions = params['group_to_questions']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, group_to_questions, timestamp)
yield (event.user_id, [list(answer) for answer in answers])
def map(student_answers):
params = context.get().mapreduce_spec.mapper.params
questions_by_usage_id = params['questions_by_usage_id']
all_answers = transforms.loads(student_answers.data)
for unit_id, unit_responses in all_answers.items():
# Is this a CourseBuilder Question/QuestionGroup set of answers?
if ('containedTypes' in unit_responses and
unit_responses['version'] == '1.5'):
for answer in event_transforms.unpack_student_answer_1_5(
questions_by_usage_id, unit_responses, timestamp=0):
yield (StudentAnswersStatsGenerator.build_key(
unit_id, answer.sequence, answer.question_id,
answer.question_type), (answer.answers, answer.score))
def process_event(cls, event, static_params):
questions_info = static_params['questions_by_usage_id']
valid_question_ids = static_params['valid_question_ids']
group_to_questions = static_params['group_to_questions']
assessment_weights = static_params['assessment_weights']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
answers = None
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
else:
logging.warning('Unexpected version %s in submit-assessment '
'event handling', version)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
else:
logging.warning('Unexpected version %s in attempt-lesson '
'event handling', version)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
if not answers:
return None
answer_dicts = []
total_weighted_score = 0.0
for answer in answers:
if not isinstance(answer.answers, (tuple, list)):
stringified_answers = [unicode(answer.answers)]
else:
stringified_answers = [unicode(a) for a in answer.answers]
answer_dict = {
'question_id': answer.question_id,
'responses': stringified_answers,
}
answer_dict['score'] = float(answer.score)
answer_dict['weighted_score'] = float(answer.weighted_score)
total_weighted_score += answer.weighted_score
answer_dicts.append(answer_dict)
submission = {
'timestamp': answers[0].timestamp,
'answers': answer_dicts,
}
submission['weighted_score'] = total_weighted_score
assessment = {
'unit_id': str(answers[0].unit_id),
'lesson_id': str(answers[0].lesson_id),
'submissions': [submission],
}
return assessment
